Description:
FORM 2
THE PATENTS ACT, 1970
(39 of 1970)
&
THE PATENTS RULES, 2003
COMPLETE SPECIFICATION
[See section 10, Rule 13]

“A SYSTEM AND METHOD FOR PROVIDING PERSONALIZED GUIDANCE DURING IMAGE CAPTURING”

Rajeev Kumar of Delhi Technological University, Bawana Road, Shahbad Daulatpur Village, New Delhi, Delhi, India, 110042; and
Dr B R Ambedkar National Institute of Technology Jalandhar an Indian Institute of G.T Road, Amritsar Bypass, Jalandhar, Punjab, India-144008

THE FOLLOWING SPECIFICATION PARTICULARLY DESCRIBES THE INVENTION AND THE MANNER IN WHICH IT IS TO BE PERFORMED.

FIELD OF THE INVENTION:
[001] The field of the present invention relates to a system and a method for providing personalized guidance during image capturing.

BACKGROUND OF THE INVENTION:
[002] In light of the evolving camera technology and the widespread use of camera-equipped mobile devices, individuals are now snapping more digital photos than ever. However, a significant portion of these users lacks formal photographic training, leading to a proliferation of subpar digital images. These images often suffer from issues like inadequate lighting and improper camera angles, ultimately resulting in aesthetically displeasing photographs. Consequently, the quest to capture that elusive perfect shot remains an ongoing endeavor

[003] Here, several prior art references are provided for photography assistance and guidance systems. US Patent No. 10,785,406 discloses a photography assistance device that provides audio feedback to users during picture capturing. However, it does not analyze the background to provide personalized tips. Further, US Patent No. 8,482,880 discloses an augmented reality system that overlays arrows to guide camera positioning. However, the guides are generalized and not specific to the background.

[004] US Patent No. 7,657,128 describes an imaging device that provides pictorial guides for common photographic situations. However, it does not use artificial intelligence or computer vision to intelligently recognize background and provide relevant tips. CN Patent No. 106,571,525 discloses a photography system that identifies obstacles in the foreground and prompts the user to change position. However, it does not address analyzing background or location type.

[005] Visual understanding is a technology for recognizing and processing objects in the manner of a human visual system and includes object recognition, object tracking, image searching, person recognition, scene understanding, spatial understanding, and image enhancement. However, none of the above prior art provides intelligent customized guidance based on identifying the type of background in the camera view.

[006] None of the existing prior arts solves the above problems and no one specifically recognizes the background of a scene to retrieve and provide corresponding personalized photography tips and guides. Hence, there is a need in the art to provide an image capturing device for personalized guidance.

SUMMARY OF THE INVENTION:
[007] This Summary is provided to introduce a selection of concepts in a simplified form that are further described below in the Detailed Description. This Summary is not intended to identify key or essential features of the claimed subject matter, nor is it intended to be used as an aid in determining the scope of the claimed subject matter.

[008] Accordingly, the present invention provides a system for providing personalized guidance during image capturing. The system comprises an image capturing module configured to sense an image. The system further comprises an image processing module comprising a processing unit configured to analyze the sensed image to extract a first information; access a database having plurality of pre-stored images; and retrieve from the database one or more pre-stored images based on the first information. The image processing module further comprises a machine learning module configured to generate a second information on the basis of the one or more pre-stored images thus retrieved. The system further comprises a recommendation module communicably coupled to image processing module. The recommendation module is configured to generate at least one recommendation on the basis of the second information retrieved from the machine learning module; and provide the at least one recommendation to a user as personalized guidance during image capturing.

[009] In an embodiment of the invention, the first information corresponds to a background of the sensed image and a foreground of the sensed image and/or a location of the user.

[0010] In another embodiment of the invention, the foreground of the sensed image corresponds to at least one of: a pose of a person forming the foreground part of the sensed image; a number of people present in the foreground part of the sensed image; and an outfit of the person present in the foreground part of the sensed image.

[0011] In yet another embodiment of the invention, the second information corresponds to one or more parameters related to the one or more pre-stored images thus retrieved.

[0012] In still another embodiment of the invention, the one or more parameters corresponds to at least one of: an angle of an image capturing module with respect to the person present in the foreground part of the one or more pre-stored images; a distance of the image capturing module from the person present in the foreground part of the one or more pre-stored images; a distance of the image capturing module with respect to the background part of the one or more pre-stored images; an angle of the image capturing module with respect to the background part of the one or more pre-stored images; a mode of the image capturing module; and a settings of the image capturing module.

[0013] In a further embodiment of the invention, the recommendation module provides at least one recommendation to the user in the form of personalized guidance including at least one of visual guidance, and/or text guidance, and/or audio guidance.

[0014] In a furthermore embodiment of the invention, the personalized guidance includes at least one of: a guidance to move the image capturing module from the person present in the foreground part of the sensed image based on the corresponding second information; a guidance to apply the mode/settings of the image capturing module based on the corresponding second information; a guidance to change a pose of the person forming the foreground part of the sensed image; a guidance to increase a number of persons forming the foreground part of the sensed image; a guidance to decrease a number of persons forming the foreground part of the sensed image; and changing a positional relation between two or more people forming the foreground part of the sensed image.

[0015] In another embodiment of the invention, the guidance to move the image capturing module from the person present in the foreground part of the sensed image comprises at least one of: a guidance to change a distance between the foreground part of the sensed image and the image capturing module; and a guidance to change an angle between the background part of the sensed image and the image capturing module.

[0016] In yet another embodiment of the invention, the recommendation module prompts the user to confirm the first information and/or to provide one or more probable suggestion on the first information.

[0017] In still another embodiment of the invention, the recommendation module is communicably coupled to at least one of a display unit for displaying the visual guidance and/or the text guidance; and an audio output unit for providing the audio guidance.

[0018] In a further embodiment of the invention, the recommendation module generates at least one recommendation further on the basis of at least one of: a number of likes received in relation to the pre-stored image; a number of photos substantially resembling the pre-stored image; and a user feed-back in relation to the pre-stored image.

[0019] The invention further provides a method for providing personalized guidance during image capturing, said method comprising: sensing an image and analyzing the sensed image to extract a first information. The method further comprises accessing a database having plurality of pre-stored images and retrieving from the database one or more pre-stored images based on the first information. The method further comprises generating a second information on the basis of the one or more pre-stored images thus retrieved; generating at least one recommendation on the basis of the second information; and providing the at least one recommendation to a user as personalized guidance during image capturing.

[0020] To further clarify the advantages and features of the invention, a more particular description of the invention will be rendered by reference to specific embodiments thereof, which is illustrated in the appended drawings. It is appreciated that these drawings depict only typical embodiments of the invention and are therefore not to be considered limiting of its scope. The invention will be described and explained with additional specificity and detail with the accompanying drawings.

BRIEF DESCRIPTION OF THE FIGURES:
[0021] In order that the invention may be readily understood and put into practical effect, reference will now be made to exemplary embodiments as illustrated with reference to the accompanying drawings, where like reference numerals refer to identical or functionally similar elements throughout the separate views. The figures together with a detailed description below, are incorporated in and form part of the specification, and serve to further illustrate the embodiments and explain various principles and advantages, in accordance with the present invention where:

[0022] Figure 1 illustrates a block diagram of the system in accordance with an embodiment of the invention;

[0023] Figure 2 illustrates a flow chart of the process for providing the personalized guidance in accordance with the embodiment of the invention;

[0024] Figure 4 illustrates an example showing the image capturing device for personalized guidance of the image capturing device in accordance with the second embodiment of the invention; and

[0025] Figure 5 illustrates a method for an image capturing device for providing personalized guidance in accordance with the second embodiment of the invention.

[0026] It may be noted that to the extent possible, like reference numerals have been used to represent like elements in the drawings. Further, skilled artisans will appreciate that elements in the drawings are illustrated for simplicity and may not have been necessarily been drawn to scale. For example, the dimensions of some of the elements in the drawings may be exaggerated relative to other elements to help to improve understanding of aspects of the present invention. Furthermore, one or more elements may have been represented in the drawings by conventional symbols, and the drawings may show only those specific details that are pertinent to understanding the embodiments of the present invention so as not to obscure the drawings with details that will be readily apparent to those of ordinary skill in the art having the benefit of the description herein.

DETAILED DESCRIPTION OF THE INVENTION:
[0027] For the purpose of promoting an understanding of the principles of the invention, specific language will be used for describing the same. It will nevertheless be understood that no limitation of the scope of the invention is thereby intended, such alterations and further modifications being contemplated as would normally occur to one skilled in the art to which the invention relates. It will be understood by those skilled in the art that the foregoing general description and the following detailed description are explanatory of the invention and are not intended to be restrictive thereof

[0028] It will be understood by those skilled in the art that the foregoing general description and the following detailed description are explanatory of the invention and are not intended to be restrictive thereof.

[0029] As used in the description herein and throughout the claims that follow, the meaning of “a,” “an,” and “the” includes plural reference unless the context clearly dictates otherwise. Also, as used in the description herein, the meaning of “in” includes “in” and “on” unless the context clearly dictates otherwise.

[0030] Reference throughout this specification to “an aspect”, “another aspect” or similar language means that a particular feature, structure, or characteristic described in connection with the embodiment is included in at least one embodiment of the present invention. Thus, appearances of the phrase “in an embodiment”, “in another embodiment”, and similar language throughout this specification may, but do not necessarily, all refer to the same embodiment.

[0031] The terms "comprises", "comprising", or any other variations thereof, are intended to cover a non-exclusive inclusion, such that one or more devices or sub-systems or elements or structures or components proceeded by "comprises... a" does not, without more constraints, preclude the existence of other devices or other sub-systems or other elements or other structures or other components or additional devices or additional sub-systems or additional elements or additional structures or additional components.

[0032] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one ordinary skilled in the art to which this invention belongs. The device, methods, and examples provided herein are illustrative only and not intended to be limiting.

[0033] The use of any and all examples, or exemplary language (e.g. “such as”) provided with respect to certain embodiments herein is intended merely to better illuminate the invention and does not pose a limitation on the scope of the invention otherwise claimed. No language in the specification should be construed as indicating any non-claimed element as being essential to the practice of the invention.

[0034] Groupings of alternative elements or embodiments of the invention disclosed herein are not to be construed as limitations. Each group member can be referred to and claimed individually or in any combination with other members of the group or other elements found herein. One or more members of a group can be included in, or deleted from, a group for reasons of convenience and/or patentability. When any such inclusion or deletion occurs, the specification is herein deemed to contain the group as modified thus fulfilling the written description of all Markush groups used in the appended claims.

[0035] Throughout the specification, when a part "includes" an element, it is to be understood that the part additionally includes other elements rather than excluding other elements as long as there is no particular opposing recitation. Also, the terms such as " . . . unit", "module", or the like used in the specification indicate a unit, which processes at least one function or motion, and the unit may be implemented by hardware or software, or by a combination of hardware and software.

[0036] In the following disclosure, an "Image capturing device" may be construed as a portable electronic device having a camera, and can be, for example, a smart phone, a tablet PC, a notebook PC, a digital camera, etc. The terms or words used hereinafter should not be construed as having common or dictionary meanings, but should be understood as having meanings and concepts that comply with the technical spirit of this disclosure. Accordingly, the following description and drawings illustrate only example embodiments of this disclosure, and do not represent the entire technical spirit of this disclosure. It should be understood that a variety of equivalents and modifications capable of replacing the embodiments may exist at the time of filing of this application.

[0037] According to an embodiment, the background may a building, a statue, a picture, or a rock, a plant such as a tree or a flower, a landscape such as a sea, a mountain, or a sunset, etc.

[0038] Embodiments of the present invention will be described below in detail with reference to the accompanying drawings.

[0039] An embodiment of the present invention discloses a system (100) for providing personalized guidance as illustrated in Figure 1 in accordance with an embodiment of the invention. Figure 1 illustrates block diagram of the system (100) according to one embodiment of the present disclosure. The system (100) may include an image capturing module (102) configured to sense an image. The system (100) further comprises an image processing module (104) that processes the sensed image. The image processing module (104) is communicably coupled to the image capturing module (102), receive the sensed image from the image capturing module (102) and process the sensed image.

[0040] The image processing module (104) by way of a non-limiting example, comprises a processing unit (106) and a machine learning module (108) that are communicably coupled to each other. The processing unit (106) is generally adapted to analyze the sensed image to extract a first information; access a database (116) having plurality of pre-stored images; and retrieve from the database (116) one or more pre-stored images based on the first information. The machine learning module (108) on the other hand is configured to generate a second information on the basis of the one or more pre-stored images thus retrieved by the processing unit (106) from the database (116). The database (116) may take the form of a disk, RAM, ROM, or flash memory, etc. The database may be a remote (cloud database).

[0041] The system (100) further comprises a recommendation module (110) communicably coupled to image processing module (104). The recommendation module (110) is generally configured to generate at least one recommendation on the basis of the second information retrieved from the machine learning module (108); and provides the at least one recommendation to a user to provide personalized guidance during image capturing.

[0042] The system (100) further comprises to at least one of a display unit (112) and/or an audio output unit (114) that is communicably coupled to the recommendation module. The display unit (112) and/or the audio output unit (114) provides the recommendation to the user. The system may additionally comprise an input unit (118) which will receive user inputs. The input unit (118) is communicably coupled to the image processing module (104) and the recommendation module (110). In an embodiment of the invention, the display unit (112) also may function as the input unit (118), for example, in case the display unit being a touch panel. The input unit (118) and the audio output unit (114) may together be fabricated as an audio input / output unit such as a speaker with a microphone.

[0043] Now referring to Figure 2, there is illustrated a flow chart of the process involved in providing the personalized guidance during image capturing. The process starts with sensing (202) an image by the image capturing module (102). The image thus sensed by the image capturing module is transferred to the image processing module (104). The image processing module (104), which in turn comprises a processing unit (106) analyzes (204) the sensed image to extract a first information. The first information corresponds to a background of the sensed image and a foreground of the sensed image and/or a location of the user. After retrieving the first information, the processing unit (106) accesses (206) a database having a plurality of pre-stored images. The processing unit (106) retrieves (208) from the database one or more pre-stored images based on the first information.

[0044] It may be noted that the foreground of the sensed image corresponds to at least one of: a pose of a person forming the foreground part of the sensed image; a number of people present in the foreground part of the sensed image; and an outfit of the person present in the foreground part of the sensed image.

[0045] Once the processing unit (106) has retrieved one or more pre-stored images from the database, the same is provided to the machine learning module (108). After receiving the one or more pre-stored image that match with the first information (which is extracted from the sensed image), the machine learning module generates a second information. The second information is thus generated on the basis of the one or more pre-stored images thus retrieved. In an embodiment of the invention, the second information which is generated by the machine learning module (108) corresponds to one or more parameters related to the one or more pre-stored images thus retrieved.

[0046] By way of a non-limiting example, the one or more parameters corresponds to at least one of: an angle of an image capturing module (102) with respect to the person present in the foreground part of the one or more pre-stored images; a distance of the image capturing module (102) from the person present in the foreground part of the one or more pre-stored images; a distance of the image capturing module (102) with respect to the background part of the one or more pre-stored images; an angle of the image capturing module (102) with respect to the background part of the one or more pre-stored images; a mode of the image capturing module (102); and a setting of the image capturing module (102).

[0047] Now the image processing module (104) and more particularly, the machine learning module (108) transfers the second information thus retrieved, to a recommendation module (110). The recommendation module (110) is configured to generate at least one recommendation on the basis of the second information retrieved by the machine learning module (108). The recommendation thus generated by the recommendation module (110) is provided to a user in the form of personalized guidance during image capturing. The recommendation can be provided to the user via any of the display unit (112) and/or an audio output unit (114).

[0048] Let’s assume that a person is intending to capture an image in front of two different types of backgrounds. For instance, the person intends to capture an image in front of Taj Mahal as shown in Figure 3 or alternatively in front of Qutub Minar as shown in Figure 4. Thus, the image capturing module senses the image, as the case may be.

[0049] Now in case the sensed image is as per Figure 3, then the sensed image is analyzed by the image processing module (104) and more particularly the processing unit (106) to extract a first information. The first information corresponds to the background of the sensed image i.e. Taj Mahal or a foreground of the sensed image i.e. a single person and/or a location of the user for instance a GPS coordinates. The image processing module (104) identifies the first information from the sensed image using artificial intelligence-based techniques. It is advantageous to implement artificial intelligence-based technique for the purposes of identification of the first information for the reasons that the artificial intelligence-based technique has high accuracy of extraction of the first information from the sensed image. It should be noted that the image processing module (104) can instead of extracting the background (i.e. the first information) as “Taj Mahal” for Figure 3, may wrongfully extract the background (i.e. the first information) as “Qutub Minar”. This scenario is completely avoided when the artificial intelligence-based technique for the purposes of identification of the first information is implemented.

[0050] Once the first information is extracted, the image processing module (104) and more particularly, the processing unit (106) accesses a database (116). The database has stored thereupon plurality of images. For instance, the database (116) can contain plurality of images corresponding to Taj Mahal. The processing unit (106) retrieves at least one pre-stored image from the database (116) which matches with the first information. For instance, as shown in Figure 5, the processing unit (106) may retrieve four pre-stored images of Taj Mahal from the database (116).

[0051] Now the machine learning module analyzes each of the four images of Taj Mahal as retrieved (as per Figure 5) to generate a second information on the basis of the one or more pre-stored images thus retrieved. It may be noted that the second information generated from each of the pre-stored image may have some aspect which is peculiar or unique.

[0052] In an embodiment of the invention, the second information which is generated by the machine learning module (108) corresponds to one or more parameters related to the one or more pre-stored images thus retrieved. By way of a non-limiting example, the one or more parameters corresponds to at least one of: an angle of an image capturing module (102) with respect to the person present in the foreground part of the one or more pre-stored images; a distance of the image capturing module (102) from the person present in the foreground part of the one or more pre-stored images; a distance of the image capturing module (102) with respect to the background part of the one or more pre-stored images; an angle of the image capturing module (102) with respect to the background part of the one or more pre-stored images; a mode of the image capturing module (102); and a setting of the image capturing module (102). Thus, it can be seen that for each of the four images (A) to (D) which are shown as part of Figure 5, the second information which is generated by the machine learning module (108) may be varying.

[0053] By way of instance, the setting of the image capturing module (102) includes a setting of the flash, a setting of wide angle, a setting of panoramic view, a setting pertaining to shutter closing speed, etc.

[0054] By way of instance, the mode of the image capturing module (102) includes a night mode, a landscape mode, a single take mode, a camera ratio; a timer ON/OFF state, etc.

[0055] Now the recommendation module generates at least one recommendation on the basis of the second information retrieved from the machine learning module. The recommendation includes recommendation comprises at least one of a guidance to move the image capturing module from the person present in the foreground part of the sensed image based on the corresponding second information; a guidance to apply the mode/settings of the image capturing module based on the corresponding second information; a guidance to change a pose of the person forming the foreground part of the sensed image; a guidance to increase a number of persons forming the foreground part of the sensed image; a guidance to decrease a number of persons forming the foreground part of the sensed image; and changing a positional relation between two or more people forming the foreground part of the sensed image.
[0056] By way of a non-limiting example, the guidance to move the image capturing module from the person present in the foreground part of the sensed image comprises at least one of: a guidance to change a distance between the foreground part of the sensed image and the image capturing module; and a guidance to change an angle between the background part of the sensed image and the image capturing module.

[0057] It may be noted that the recommendation module generates at least one recommendation further on the basis of at least one of: a number of likes received in relation to the pre-stored image; a number of photos substantially resembling the pre-stored image; and a user feed-back in relation to the pre-stored image. Thus, apart from relying upon the machine learning module, the recommendation module may take into consideration further parameters (which are not generated by the machine learning module) while providing the recommendation.

[0058] It may be noted that after the recommendation module providing the recommendation to the user, may prompt the user to confirm the first information and/or to provide one or more probable suggestion on the first information. The system keeps an active track of the image captured by the user and matches the same with the recommendation thus provided. In case the image captured is different from the recommendation thus provided, the system may cause the newly captured image to be stored in the database (for instance after seeking suitable approvals from the user). Alternatively, the system may cause the generating of second information from the image thus captured by the user and store the second information (for instance, after seeking suitable approvals from the user). Thus, the system is self-learning in the sense of finding out variations in the second information and storing different set of second information in the database for the same set of background information.

[0059] Conversely, the 'second information' represents the outcome derived from the database, rooted in the machine learning process. The second information encompasses various forms of guidance, including visual, textual, and auditory guidance.

[0060] Now referring to Figure 6, if the image being sensed is as per (A) and recommendation is as per (B), then the system can provide recommendation in the form of R1 and R2, which indicate that the image capturing module has to be moved in sideways (towards the right side) and the image capturing module has to be moved towards the user. Now referring to Figure 7, if the image being sensed is as per (A) and recommendation is as per (B), then the system can provide recommendation in the form of R3 and R4, which indicate that the image capturing module has to be moved in sideways (towards the left side) and the image capturing module has to be moved away from the user. While the recommendation in relation to the options provided in Figure 5 (C) and Figure 5 (D) are not shown, it may be appreciated that the system can generate suitable recommendation in relation to each desired end result and provide the same to the user.

[0061] It may be noted that the database (116) may store a boot program and one or more operating systems and applications. The operating system may function as an interface between hardware and software applications, as a coordinator between disparate software applications, and generally manages computer resources, such as the central processing unit ("CPU"), graphics processing unit ("GPU"), main memory unit, and the database (116). Applications may be divided into embedded applications, which are pre-installed in the mobile terminal and are generally not removable, and third-party applications, which are installed in the mobile terminal by end-users, and are generally less critical to the core functions of the mobile terminal.

[0062] Additionally, the database contains a wealth of photography tips and tricks, alongside high-quality photos taken at similar locations or under similar circumstances. On the basis of location and image category such as solo, couple, family, or group photo, the machine learning process compares the real time background with information stored in database. The machine learning process selects the most relevant photography tips based on the analysis of the scene and transforms them into easily understandable text-visual overlays or audio guidance.

[0063] The display unit (114) displays the second information in the form of visuals on a screen. The display unit (114) may be implemented utilizing, for example, a Liquid Crystal Display (LCD), Organic Light Emitted Diodes (OLED), Active Matrix Organic Light Emitted Diodes (AMOLED), or a flexible display. The display unit (114) displays data on a screen under the control of the recommendation module (112). The control unit (104) may determine a user gesture as one of a touch, a multi-touch, a tap, a double tap, a long tap, a tap & touch, a drag, a flick, a press, a "pinch in," and a" pinch out" based on the touch coordinates, whether or not a touch of the touch input means has been released, whether or not the touch input means has been moved, a change in the location of the touch input means, and a transfer speed of the touch input means.

[0064] In an embodiment of the present invention, a machine learning process analyses the background and identifies the location of the user and of the images stored in the database. The machine learning process encompasses a series of steps and tasks designed to develop, train, evaluate, and deploy machine learning models. The primary goal of a machine learning process is to create models that can make predictions, classifications, or decisions based on data without being explicitly programmed.

[0065] In an embodiment of the invention, the image processing module processes the live image capturing device view to identify the background, people in the frame, and their positions relative to the image capturing device. The image processing module enhances transforms and extracts information from images to make them more suitable for various applications or to gain insights from the visual data.

[0066] The invention may utilize a comprehensive collection of images, meticulously categorized by the machine learning process encompassing categories such as solo, couple, family, or group photos, and varying in terms of angles and poses resides within the database. Furthermore, these stored images can also be dynamically extracted in real-time, either from a server or from social media platforms, leveraging the insights gained through the machine learning process's background analysis.

[0067] Alternatively, in another embodiment of this invention, real-time image extraction from either a server or social media platforms is facilitated, contingent upon the background identified by the machine learning process. The images are also extracted in real-time from the server, or from social media based on identified background.

[0068] In an embodiment of the invention, the database is categorized by distinct location types including cafes, beaches, monuments, parks, and more by machine learning process. These databases also consider various photography categories. In real-time, the user utilize text and visual overlays to direct users in adjusting the image capturing device's position, distance, and angle, all in pursuit of achieving the most optimal photo composition. Once the recommended adjustments to the image capturing device have been implemented, these overlays gracefully vanish. The image capturing device serves as an intelligent and personalized photography assistant, expertly tailored to the backdrop's nuances, rendering it an invaluable tool for users.

[0069] In an embodiment of the invention, the system (100) tailored explicitly for personalized guidance, aimed at enhancing users' photography skills. This system (100) harnesses advanced an image processing modules and the machine learning process to scrutinize the background within the live image capturing device view, all while considering the user's presence. Additionally, it takes into account the image capturing device's location and determines the precise type of environment be it a renowned tourist attraction, a restaurant, or a university campus. Building upon the situational analysis, the machine learning process within the device retrieves pertinent photography tips and techniques from the database, featuring relevant photographs of similar locations and categories, including solo, couple, family, or group photos. The extracted insights are then seamlessly transformed into user-friendly text-visual overlays or auditory guidance, which are superimposed onto the image capturing device (100), making photography guidance both accessible and comprehensible.

[0070] An advantage of the present invention is that a personalized guidance is received to the user. Yet an advantage of the present invention is that aesthetically improved picture.

[0071] While specific language has been used to describe the disclosure, any limitations arising on account of the same are not intended. As would be apparent to a person in the art, various working modifications may be made to the method in order to implement the inventive concept as taught herein.

[0072] The figures and the forgoing description give examples of embodiments. Those skilled in the art will appreciate that one or more of the described elements may well be combined into a single functional element. Alternatively, certain elements may be split into multiple functional elements. Elements from one embodiment may be added to another embodiment. For example, orders of processes described herein may be changed and are not limited to the manner described herein. Moreover, the actions of any flow diagram need not be implemented in the order shown; nor do all of the acts necessarily need to be performed. Also, those acts that are not dependent on other acts may be performed in parallel with the other acts. The scope of embodiments is by no means limited by these specific examples.
, C , Claims:WE CLAIM:

1. A system (100) for providing personalized guidance during image capturing, the system (100) comprising:
an image capturing module (102) configured to sense an image;
an image processing module (104) comprising:
• a processing unit (106) configured to:
• analyze the sensed image to extract a first information;
• access a database having plurality of pre-stored images; and
• retrieve from the database one or more pre-stored images based on the first information; and
• a machine learning module (108) configured to generate a second information on the basis of the one or more pre-stored images thus retrieved; and
a recommendation module (110) communicably coupled to image processing module (104); the recommendation module configured to:
• generate at least one recommendation on the basis of the second information retrieved from the machine learning module; and
• provide the at least one recommendation to a user as personalized guidance during image capturing.

2. The system as claimed in claim 1, wherein the first information corresponds to a background of the sensed image and a foreground of the sensed image and/or a location of the user.

3. The system as claimed in claim 1, wherein the foreground of the sensed image corresponds to at least one of:
a) a pose of a person forming the foreground part of the sensed image;
b) a number of people present in the foreground part of the sensed image; and
c) an outfit of the person present in the foreground part of the sensed image.

4. The system as claimed in claim 1, wherein the second information corresponds to one or more parameters related to the one or more pre-stored images thus retrieved, wherein the one or more parameters corresponds to at least one of:
a) an angle of an image capturing module (102) with respect to the person present in the foreground part of the one or more pre-stored images;
b) a distance of the image capturing module (102) from the person present in the foreground part of the one or more pre-stored images;
c) a distance of the image capturing module (102) with respect to the background part of the one or more pre-stored images;
d) an angle of the image capturing module (102) with respect to the background part of the one or more pre-stored images;
e) a mode of the image capturing module (102); and
f) a setting of the image capturing module (102).

5. The system as claimed in claim 1, wherein the recommendation module (110) provides at least one recommendation to the user is provided in the form of personalized guidance including at least one of visual guidance, and/or text guidance, and/or audio guidance, wherein the personalized guidance includes at least one of:
a) a guidance to move the image capturing module (102) from the person present in the foreground part of the sensed image based on the corresponding second information;
b) a guidance to apply the mode/settings of the image capturing module (102) based on the corresponding second information;
c) a guidance to change a pose of the person forming the foreground part of the sensed image;
d) a guidance to increase a number of persons forming the foreground part of the sensed image;
e) a guidance to decrease a number of persons forming the foreground part of the sensed image; and
f) changing a positional relation between two or more people forming the foreground part of the sensed image.

6. The system as claimed in claim 5, wherein the guidance to move the image capturing module (102) from the person present in the foreground part of the sensed image comprises at least one of:
a. a guidance to change a distance between the foreground part of the sensed image and the image capturing module (102); and
b. a guidance to change an angle between the background part of the sensed image and the image capturing module (102).

7. The system as claimed in claim 1, wherein the recommendation module (110) prompts the user to confirm the first information and/or to provide one or more probable suggestion on the first information.

8. The system as claimed in claim 5, wherein the recommendation module (110) is communicably coupled to at least one of a display unit (112) for displaying the visual guidance and/or the text guidance; and an audio output unit (114) for providing the audio guidance.

9. The system as claimed in claim 1, wherein the recommendation module (110) generate at least one recommendation further on the basis of at least one of:
a number of likes received in relation to the pre-stored image;
a number of photos substantially resembling the pre-stored image; and
a user feed-back in relation to the pre-stored image.

10. A method for providing personalized guidance during image capturing, said method comprising:
sensing (202) an image;
analyzing (204) the sensed image to extract a first information;
accessing (206) a database having plurality of pre-stored images;
retrieving (208) from the database one or more pre-stored images based on the first information;
generating (210) a second information on the basis of the one or more pre-stored images thus retrieved;
generating (212) at least one recommendation on the basis of the second information; and
providing (214) the at least one recommendation to a user as personalized guidance during image capturing.