Claims:We Claim:
1. A method for processing images, the method comprising:
a. analyzing an image provided at a third-party website in connection with displaying concurrently one or more other images at the third-party website that satisfy a search criterion generated from analyzing the image, wherein analyzing the image includes:
b. programmatically analyzing the image to determine one or more visual characteristics of at least one object in the image;
c. generate a search criterion based at least in part on the one or more visual characteristics of the at least one object;
d. performing a search of a data store using the search criterion in order to obtain a search result that includes one or more images of merchandise objects that satisfy the search criterion; and
e. presenting the one or more images of merchandise objects with the analyzed image at the third-party website;
2. The method of claim 1, wherein metadata associated with the image to determine the one or more categories.
3. The method of claim 1, further comprising segmenting the analyzed image to cause the at least one object of the analyzed image to be segmented apart from a remainder of the analyzed image to be selectable by a user to generate said search criterion corresponding to the selected object.
4. The method of claim 1, wherein the third-party website corresponds to a blog.
5. The method of claim 1, wherein the one or more visual characteristics of the analyzed image include a global feature.
6. The method of claim 1, wherein the one or more visual characteristics of the analyzed image include a local feature.
7. The method of claim 1, wherein the one or more visual characteristics of the analyzed image include (i) a global colour of the at least one object, or (ii) a localized color of a portion of the at least one object.
8. The method of claim 1, wherein the one or more visual characteristics of the analyzed image include a texture, a pattern, or a shape of the at least one object.
, Description:Technical Field of the Invention
The present invention related to the field of digital image processing in a website
Background of the Invention
Digital photography has become a consumer application of great significance. It has afforded individuals convenience in capturing and sharing digital images. Devices that capture digital images have become low-cost, and the ability to send pictures from one location to the other has been one of the driving forces in the drive for more network bandwidth.
Due to the relative low cost of memory and the availability of devices and platforms from which digital images can be viewed, the average consumer maintains most digital images on computer-readable mediums, such as hard drives, CD-Roms, and flash memory. The use of file folders is the primary source of organization, although applications have been created to aid users in organizing and viewing digital images. Some search engines, such as GOOGLE, also enables users to search for images, primarily by matching text-based search input to text metadata or content associated with images.
Object of the Invention
The primary object of the present invention is to provide a system & method for searching image on a website
Another object of the present invention is image searching using manual enrichment, classification, and segmentation
Summary of the Invention
The embodiments are described herein for the use of digital images and photography. Embodiments described herein enable programmatic detection and/or identification of various types and classes of objects from images, including objects that are items of commerce or merchandise. Among the numerous embodiments described herein, embodiments include (i) systems and methods for detecting and analyzing images; (i) systems and methods searching for images using image data, text data, features, and non-textual data; (iii) user-interface and features thereof for enabling various forms of search on a collection or database of analyzed images; (iv) e-commerce applications for enabling visual, non-textual and visually aided searches of merchandise items; and (v) retrieval and analysis of images from third-part y sites and network locations. Embodiments described herein further include components, modules, and sub-processes that comprise aspects or portions of other embodiments described herein.
Embodiments described herein provide for a system for creating a data collection of recognized images. The system includes an image analysis module that is configured to programmatically analyze individual images in a collection of images in order to determine information about each image in the collection. The system may also include a manual interface that is configured to (i) interface with one or more human editors, and (ii) displays a plurality of panels concurrently. Individual panels may be provided for one or more analyzed images, and individual panels may be configured to display information that is at least indicative of the one or more images of that panel and/or of the information determined from the one or more images. Additionally, the manual interface enables the one or more human editors to view the plurality of panels concurrently and to interact with each of the plurality of panels in order to correct or remove any information that is incorrectly determined from the image of that panel.
One or more embodiments enable image analysis of content items that include image. Among other applications, the analysis of such content items (including images or images with text and/or metadata) enables the use of content or image-based searching. In one embodiment, a search query may be derived from image data, or values for image data.
Brief Description of Drawings:
FIG. 1 A method in which image segmentation and alignment may be performed using a statistical analysis, according to one or more embodiments described herein.
Detailed Description of Invention:
Image Segmentation and Alignment
FIG. 1 illustrates a method in which image segmentation and alignment may be performed using a statistical analysis, according to one or more embodiments described herein. A method such as described by an embodiment of FIG. 1 may be executed as part of the image segmentizer of system 100, In step 510, a programmatic statistical analysis of a pixel distribution is applied to an image to determine foreground and background elements of the image. Let I be an image. Let F be the features of the image for each pixel. Examples of features are grayscale intensities, values in RGB color space, CIE-L*a*b* or any other color space, or texture and other image features that are derived from the raw image data. Let P be a set of pixels that are at a distance of k pixels from the image boundary. Let m be the median vector of the feature vectors for the pixels belonging to set P. Under one implementation, the following algorithm can be used for segmentation:
a. For each pixel (x, y), a calculation is made of the feature distance from m: d (x, y) =?F (x, y) -m?^2.
b. Label all pixels for which d (x, y) =T as foreground. T is a predefined threshold.
c. A connected component analysis is performed for the background pixels. Connected components are identified when they are of size less than M and label pixels belonging to these connected components as foreground. M is assumed to be a predefined threshold.
d. A connected component analysis is performed for the foreground pixels. Connected components are identified that are of size less than M. Pixels belonging to these connected components are labeled as background.
e. As an additional option, a connected component analysis is performed on the background to identify connected components that do not touch the image boundary. The pixels belonging to these connected components are labeled as foreground.
f. If the foreground or background region size is less than n percentage of the full image size, the segmentation algorithms and processes are performed again, with different parameters, until this condition is invalid. The value of n is a predefined threshold.
The final labeling returns a map in which each pixel is labeled either as foreground or background. The foreground pixels define the segmented image 115 and can then be used for alignment and feature extraction, as performed by other modules of system 100. As an addition or alternative, other segmentation algorithms may be performed. Among such algorithms are Min-cut Max-flow based segmentation, Random walk and first arrival probabilities-based image segmentation, Curve evolution and active contours.