Description:FIELD OF THE INVENTION
Hence, the area of application of Content-Based image Retrieval (CBIR) is high accuracy of image search and retrieval. More precisely, it focuses on the image retrieval performance achieved by combining the feature-extraction based on the U-Net and the dynamic feature re-weighting scheme combination. The invention is also a powerful tool to the general area of domains, e.g., general image data sets and medical image retrieval, where the indexing and searching of a correct image in a rapid and stable way is needed.
BACKGROUND OF THE INVENTION
As a result of the rapid expansion of the number of digital imaging sources, the need for a powerful system providing immediate image retrieval with concomitant enormous number of image datasets has grown dramatically. CBIR systems are generally designed for the capabilities of the tester to locate and extract an image knowing its content (e.g., texture, colour, form, image content, and so on) rather than using metadata (e.g., keywords and tags). CBIR based systems are also irreplaceable in several areas—digital media, surveillance, medical imaging, and remote sensing. However, image sets remain in the back of not only in quantity, but also in the diversity of image sets, at present CBIR systems are still restricted by the retrieval accuracy, the scalability, and the discriminant power in specific niche domains (i.e., biomedical image retrieval).

Most of the past work in developing CBIR system has revolved around using low-level features such as color histograms, shapes and texture patterns. These approaches have limitations, as they often fail to picture appropriate contextual relationships within images. For example , in medical imaging, two images of a given anatomical region may have very different pixel-level characteristics, perhaps because the same fundamental aetiological problem exists; hence, low-level feature correspondence is not likely to be fruitful matching. As a result, feature extraction as well as feature illustration are important factors which influence the performance capabilities of a CBIR system and the justification for the need for such methods which will adequately capture contextual information has become stronger.

With the introduction of CNN to the image analysis field, image analysis domain has been brought to an advanced level mainly due to the successful use of models like ResNet, VGG, and AlexNet, which have learnt complex, florid, hierarchical features in an image at a low level, which are not achieved with the generic, manually designed features. Although, when CNNs are applied in an effort to learn highlevel visual representation, it is not necessarily correct to apply CNNs in an image retrieval task when applied to the case of images of highdimension and large size. Especially, the conventional CNN based CBIR system is significantly insufficient to adapt to the query heterogeneity of the users, i.e. Generally, such systems are characterized by a fixed feature representation, which is independent of query context as well as the relative importance of a specific feature.
U-Net's encoder-decoder structure allows it to capture both local and global features of an image to retain spatial hierarchies makes it particularly well-suited for image interpretation. Unlike traditional CNNs, U-Net provides a more granular understanding of an image, preserving context and finer details that are important for distinguishing between similar or related images. This characteristic of U-Net makes it a promising choice for feature extraction in CBIR systems, as it can capture higher-level features that may be crucial for distinguishing visually similar images, especially in domains like medical imaging, where subtle differences between images can be critical.
Despite the advantages of deep learning models like U-Net, existing CBIR systems still face limitations in handling feature extraction in a way that is adaptive to different query conditions. Once features are extracted, most systems treat all features with equal importance during retrieval. For example, in a medical image retrieval scenario, texture features may be more important for identifying a tumor, while in a general image retrieval task, color or shape might be more relevant. Therefore, there is a need for an adaptive mechanism that can dynamically adjust the importance of various features during the retrieval process.
The methods in CBIR often lack a system that integrates dynamic feature weighting with deep learning-based feature extraction. While some systems attempt to assign weights to features based on pre-defined rules or static models, they do not account for contextual relevance based on the specific query. This leads to a gap in retrieval performance, as many systems struggle to provide highly accurate retrieval results when faced with diverse or ambiguous queries. Additionally, many existing systems do not exploit advanced techniques like cosine similarity for determining feature importance, a mechanism that can quantify the relevance of features based on their link to the query image.
The present invention discourses these challenges by suggesting CBIR system combines the power of U-Net for feature extraction with an innovative adaptive feature re-weighting technique. This system dynamically evaluates the importance of features based on the input query and adjusts the weight of each feature accordingly. By integrating these techniques, the invention aims to create a CBIR system that is both more accurate and more flexible in handling a wide variety of retrieval scenarios, leading to better concert in terms of overall retrieval effectiveness. The proposed system also overcomes the shortcomings of traditional CBIR methods by making feature extraction and retrieval more context-aware and adaptive, thus providing a significant improvement over existing methodologies.
The adaptability of this system is particularly valuable in specialized fields such as medical image retrieval, where images are often complex and require nuanced interpretations. In such domains, small changes in image characteristics can signify crucial differences, and an adaptive CBIR system that can excellently capture and rank relevant features is essential for precise and reliable. As a result, this invention not only enhances the general CBIR process but also makes significant strides in addressing the specific needs of challenging domains like medical imaging.
Existing Methodologies in CBIR
In old CBIR systems, the process generally involves the mining of low-level features from images, followed by the comparison of these features with a query image to retrieve similar images from a database. These methods, while effective in simple and small-scale retrieval tasks, particularly in large-scale and complex datasets. Some key characteristics and limitations of existing methodologies are outlined below:
Handcrafted Feature Extraction:
Early CBIR systems relied heavily on handcrafted features like Gabor filters, and shape-based features like edge histograms. These methods were simple and computationally efficient but suffered from several drawbacks:
Limited feature representation: Handcrafted features only capture low-level information about the image, which is often insufficient for representing more abstract or high-level visual information. This becomes especially problematic in fields like medical imaging, where subtle differences in textures or shapes can be critical for accurate diagnosis.
Poor scalability: As the size of the image dataset increases, handcrafted features become inefficient and computationally expensive to compute and store. They also struggle to generalize across diverse image categories or applications.
Lack of adaptability: Traditional methods do not adapt to specific user queries or dynamically adjust the weight of different features depending on the context. This lack of adaptability leads to lower retrieval accuracy when queries vary in complexity or ambiguity.
CNN-Based Feature Extraction:
CNNs began to be employed for feature extraction in CBIR systems. CNNs, such as ResNet, VGG, and AlexNet, have shown superior performance in capturing complex hierarchical features compared to handcrafted methods. These models automatically learn high-level, discriminative features from large image datasets. However, even with CNNs, several challenges persist:
Fixed feature representation: Once features are extracted, CNNs often treat all features equally, regardless of the query's context. In other words, CNN-based CBIR systems typically use a fixed set of features without dynamically adjusting their importance based on the query image.
Inability to prioritize features: Standard CNNs do not have an integrated mechanism to re-weight features based on their relevance to a specific query. This results in a less flexible system where important features might be underemphasized or irrelevant features might be overly weighted, leading to suboptimal retrieval performance.
Limited interpretability: While CNNs are powerful, they lack the transparency of handcrafted methods in terms of understanding why certain features are important. This reduces the ability to optimize and fine-tune the system for specific tasks.
Lack of Query-Aware Adaptation:
Many existing CBIR systems do not account for the diverse nature of queries. Existing methods typically rely on fixed, pre-learned representations that do not adapt based on the specific features or importance of the query. This results in inconsistent retrieval accuracy, especially when the queries are ambiguous or require a nuanced understanding of the image content.
Projected Methodology
It introduces a novel approach that overcomes many of the limitations found in existing methodologies. The key innovations of the proposed methodology are:
U-Net for Feature Extraction:
Instead of relying solely on traditional handcrafted features or standard CNNs, the proposed CBIR system utilizes U-Net designed for image segmentation errands. U-Net has several advantages:
Hierarchical Feature Extraction: U-Net’s encoder-decoder structure captures both LL and HL features. This allows the system to better understand the image's content, making it especially useful in complex domains like medical imaging, where nuanced details are crucial.
Preservation of Spatial Context: U-Net's design preserves the spatial hierarchies of features, which is vital for ensuring that contextual information, such as the location of an object or lesion, is properly maintained during feature extraction. This is especially critical in medical imaging, where small anatomical differences can have significant diagnostic implications.
Improved Feature Representation: By utilizing a model capable of extracting rich, high-level features, the system can significantly outperform traditional handcrafted methods in terms of both feature richness and accuracy.
Adaptive FRW Mechanism:
One of the main innovations of the proposed system is the introduction of an adaptive, query-aware of FRW. This mechanism adjusts the importance of extracted features founded on the resemblance between the request image and the dataset images. Key features of this approach include:
Dynamic Weight Adjustment: Features are re-weighted based on their relevance to the query, allowing the system to stress the most vital features for the specific search context. For example, if the query involves a tumor in a medical image, texture features may be more critical, while shape-based features may be emphasized for other types of queries.
Cosine Similarity for Feature Evaluation: The re-weighting mechanism uses wighted cosine similarity to evaluate similar extracted features are to the query. This allows the system to assess the relative importance of each feature dynamically, enhancing retrieval accuracy.
Improved Precision and Recall: By focusing on the most relevant features for each query, the system achieves higher precision, better recall, and a significantly improved F1-score compared to existing systems. This leads to more accurate and contextually appropriate results.
Query-Aware Retrieval:
The proposed system incorporates a query-aware retrieval process that dynamically adjusts based on the query's content. Unlike traditional CBIR systems that rely on a static set of features, the proposed system can adapt to different query types, whether they involve detailed medical images or more general visual content. This flexibility enables the system to provide more accurate and relevant results across diverse retrieval scenarios.
Comparison Summary
Aspect Existing Methodologies Proposed Methodology
Feature Extraction Handcrafted features or CNN-based methods (fixed features) U-Net (captures both LL and HL features)
Feature Representation Limited to low-level features, lacks depth Rich, hierarchical feature representation with U-Net
Adaptability Fixed feature set, no dynamic adjustment based on queries Dynamic re-weighting of features based on query similarity
Scalability Struggles with large datasets and complex queries Scalable due to U-Net’s efficiency and feature re-weighting
Domain Flexibility Performs well for simple queries but limited for complex domains Handles both general and specialized domains (e.g., medical)
Contextual Awareness Lack’s ability to prioritize relevant features dynamically Query-aware adaptation that adjusts feature relevance

The proposed CBIR system addresses critical limitations of existing methodologies by combining the strengths of U-Net-based feature extraction with an innovative adaptive feature re-weighting approach. The result is a more robust, flexible, and accurate retrieval system that performs well across both general image datasets and more complex, specialized domains like medical imaging. This system significantly enhances retrieval performance by dynamically prioritizing relevant features based on query-specific needs. The proposed methodology represents a significant advancement over traditional CBIR systems by incorporating deep learning models that adapt to user queries.
The primary objective is to improve the effectiveness and accuracy of CBIR through an innovative integration of U-Net-based feature extraction and adaptive feature re-weighting techniques. The system is designed to meet the encounters modelled by large-scale and diverse image datasets, offering robust retrieval performance across different domains, including general image retrieval and specialized medical image retrieval. The detailed objectives of the proposed methodology are as follows:
1. Enhance Retrieval Accuracy for Diverse Image Datasets
One of the core objectives is to significantly advance the accuracy of image retrieval in a variety of image databases. Old CBIR methods, based on handcrafted features or standard CNN-based models, often struggle to capture complex and high-level semantic features, leading to poor retrieval results, especially in large or complex datasets. The proposed system addresses this by using U-Net for feature extraction, which captures both low-level and high-level features. The system is designed to handle both general image datasets (e.g., images from everyday objects, scenes, etc.) and domain-specific datasets (e.g., medical images, satellite imagery, etc.), providing high retrieval accuracy across these varied domains.
By using adaptive feature re-weighting leading to more accurate matches and better retrieval performance in both precision and recall metrics. The ability to adaptively emphasize features based on the query's needs will allow the system to retrieve highly relevant images, improving overall performance compared to traditional methods that rely on fixed feature sets.
2. Leverage U-Net Architecture for Robust Feature Extraction
A key objective is to utilize the U-Net traditionally employed in image subdivision tasks, to enhance feature extraction in the CBIR process. Unlike conventional CNNs that focus mainly on extracting general features, U-Net structure is capable of preserving spatial hierarchies and capturing both LL and HL features simultaneously.
Deep learning model is particularly beneficial for capturing more complex and detailed features of images, making it well-suited for domains where fine-grained image characteristics are essential. For instance, in medical image retrieval, where subtle differences in texture, shape, and even color may signify significant clinical conditions, U-Net's architecture ensures that the extracted features retain the spatial and contextual information needed for precise retrieval. The objective is to move beyond basic feature extraction techniques and implement a system that better understands the intricate details within each image, offering a substantial improvement over traditional method.
3. Implement Adaptive Feature Re-Weighting to Optimize Retrieval
Another significant objective is to integrate an adaptive, query-aware feature re-weighting mechanism that adjusts the importance of extracted features based on the specific query. In conventional CBIR systems, features are generally treated equally regardless of the query image’s context, which can lead to poor retrieval results, especially when the query is ambiguous or the dataset is highly diverse.
The proposed system uses a cosine similarity-based approach to grade the resemblance between the inquiry image and database images, and then dynamically adjusts the weights of different features accordingly. Texture features may be more important than shape or color features. By re-weighting features according to their relevance to the query, the system is able to prioritize the most informative features, resulting in improved retrieval performance. The adaptive re-weighting mechanism ensures that the system can efficiently handle a wide range of retrieval scenarios, whether the query is general or specialized.
4. Improve Scalability for Large-Scale Image Datasets
The efficiency and scalability of the retrieval system become critical. Traditional CBIR methods based on handcrafted features or simple CNNs often struggle to maintain performance size of the dataset growths due to the high computational cost of feature extraction and the lack of dynamic adaptation to different types of queries. The proposed methodology aims to address these issues by combining the power of U-Net-based feature extraction and adaptive re-weighting, which enables the system to handle large datasets without sacrificing retrieval accuracy.
The use of U-Net's deep learning architecture ensures that the system can process images efficiently, extracting hierarchical features while maintaining spatial context. The adaptive feature re-weighting mechanism further improves scalability by reducing the need for exhaustive feature comparison, focusing only on the most relevant features. This results in a system that can scale effectively across large image datasets, making it suitable for real-world applications where the size and diversity of image collections continue to grow.
5. Address Diverse Retrieval Scenarios with Domain-Specific Adaptability
An important objective is to develop a CBIR system that is adaptable to diverse retrieval scenarios, particularly when the images come from specialized domains like medical imaging. In such domains, images often contain fine details and subtle differences that are critical for accurate analysis. The proposed methodology aims to enhance the flexibility and adaptability of the retrieval system by adjusting the feature weights depending on the type of query. The adaptive re-weighting mechanism ensures that the system prioritizes features that are contextually relevant, thus improving retrieval precision for specialized domains. Similarly, for general image datasets, the system can adjust its focus to emphasize features like color or shape, depending on the query. This adaptability ensures that the proposed CBIR system can serve multiple domains effectively, making it a versatile tool for diverse applications.
6. Achieve Robustness.
Another important objective is to enhance robustness of the system by enhancing few techniques that improve the quality of the input data before it is processed by the feature extraction and re-weighting modules. Image pre-processing images for effective feature extraction, particularly DLL models like U-Net.
The pre-processing steps in the proposed system include image augmentation. Normalization of pixel values is also performed to standardize the input images, ensuring that the deep learning model receives images in a consistent format. These pre-processing steps are designed to improve model generalization, ensuring that the system remains robust across different datasets and avoids errors that may arise due to variations in image characteristics (e.g., lighting, scale, orientation).
7. Evaluate Performance Through Standard Metrics (Precision, Recall, F1-Score)
A crucial objective of the proposed methodology is to achieve significant improvements in standard performance metrics. Particularly in terms of how well it retrieves relevant images and how it balances false positives and false negatives.
The proposed system is designed to outperform traditional CBIR methods across these metrics. By leveraging U-Net-based feature extraction, which captures more detailed and high-level features, combined with the adaptive feature re-weighting mechanism, which prioritizes the most relevant features for each query, the system aims to enhance precision (reducing false positives), improve recall (increasing true positives), and achieve a higher F1-score (balancing precision and recall). These metrics will be used to benchmark the proposed system against existing CBIR methodologies, demonstrating its superior performance.
SUMMARY OF THE INVENTION
This summary is provided to introduce a selection of concepts, in a simplified format, that are further described in the detailed description of the invention.
This summary is neither intended to identify key or essential inventive concepts of the invention and nor is it intended for determining the scope of the invention.
To further clarify advantages and features of the present 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.
The present invention proposes a CBIR addressing the limitations of existing systems by combining U-Net for feature extraction with an innovative adaptive feature re-weighting mechanism. This invention aims to improve the retrieval accuracy, efficiency, and adaptability of CBIR, making them suitable for large-scale and diverse image datasets, including both general image retrieval and domain-specific applications such as medical image retrieval.
Key Aspects of the Invention:
U-Net-Based Feature Extraction:
At the core of the system is the use of U-Net architecture originally designed for image segmentation works. Unlike traditional CNN models that capture only fixed, high-level features, U-Net's unique encoder-decoder architecture enables the extraction of both low-LL features and HL features in an integrated manner. This feature extraction approach allows the system to capture detailed spatial hierarchies and retain crucial contextual information, making it especially useful for domains like medical imaging, where subtle differences can be critical for diagnosis.
The U-Net model is highly effective in recognizing complex image patterns, which is crucial when dealing with large image datasets that contain varied or intricate visual elements. It allows the CBIR system to extract more discriminative and informative features compared to conventional handcrafted feature-based methods, or even typical CNN architectures used in traditional CBIR systems.
Adaptive Feature Re-Weighting:
One of the key innovations of this system is the adaptive, query-aware feature re-weighting mechanism. Existing CBIR systems generally rely on a fixed set of features for all image queries, regardless of the query's content. This lack of adaptability can lead to poor retrieval results, especially when queries contain complex or ambiguous content. The proposed system addresses this by dynamically adjusting the importance of extracted features based on the context of the query image.
The re-weighting process ensures that features that are most relevant to the query are given higher importance during the retrieval process. For example, in the case of a medical image query (such as a tumor), the system can emphasize texture features over color features. This dynamic feature re-weighting enables the system to prioritize the most informative features for each specific query, improving both precision (reducing false positives) and recall (increasing true positives).
Image Pre-Processing:
To further enhance the system's robustness, the proposed method includes image pre-processing steps such as augmentation and normalization. Image augmentation involves applying various transformations. Standardization standardizes the pixel values of images, ensuring that the deep learning model receives data in a consistent format. These pre-processing steps improve the accuracy of feature mining and ensure that the system performs well across a variety of image datasets with different characteristics.
Scalability and Efficiency:
The proposed CBIR system is designed with scalability in mind, capable of efficiently handling large image datasets without sacrificing performance. By utilizing U-Net's deep learning architecture, which is well-optimized for hierarchical feature extraction, and adaptive feature re-weighting, which reduces unnecessary computation by focusing on the most relevant features, the system can maintain high retrieval accuracy even in large-scale settings. This makes the proposed methodology suitable for all applications, where image repositories are often vast and varied, and the efficiency of the retrieval process is paramount.
Improved Retrieval Concert:
It aims to provide superior performance in terms of standard retrieval metrics. Traditional CBIR methods often fall short in these metrics due to their inability to adapt to the complexity of user queries. The integration of adaptive feature weighting ensures that the retrieval process is more query-specific, thus improving the precision (minimizing irrelevant results) and recall (increasing relevant results). It is also significantly enhanced, leading to a more effective and efficient retrieval system overall.
Versatility Across Domains:
The system is not limited to general image retrieval but is also designed to perform well in specialized domains, such as medical image retrieval, where images often contain fine, subtle differences that are critical for accurate analysis. The ability to dynamically adjust the feature re-weighting based on the type of query (e.g., a medical diagnosis query vs. a general object recognition query) a wide variety of image retrieval tasks effectively. This makes the proposed system adaptable to many applications, from digital media to remote sensing, and particularly medical imaging, where precision is paramount.
Key Advantages of the Invention:
High-Accuracy Retrieval:
By combining U-Net’s advanced feature extraction with a dynamic, query-aware feature weighting mechanism, the system provides superior accuracy compared to existing CBIR methods. This results in better retrieval performance, as it can more effectively match images based on the relevance of the features, rather than using a one-size-fits-all approach.
Context-Aware Adaptability:
The integration of adaptive feature re-weighting means that the system can tailor its retrieval strategy to the specific needs of the query, rather than relying on static feature representations. This contextual awareness improves retrieval quality, especially for complex or ambiguous queries.
Efficient for Large Datasets:
The proposed system scales well with large image datasets. The use of U-Net for feature extraction, combined with adaptive feature re-weighting, optimizes both computational efficiency and storage. This makes it feasible to deploy the system in real-world applications where large-scale image databases need to be searched quickly and accurately.
Versatility Across Domains:
The system is adaptable to multiple domains, from medical image retrieval, where precision and subtle differences are critical, to general image retrieval scenarios. Its ability to prioritize relevant features based on the query type ensures its versatility and makes it applicable in various fields, including digital media, e-commerce, and geospatial data analysis.
Improved Performance Metrics:
The use of cosine similarity for feature evaluation and adaptive weighting enhances the precision, recall, and F1-score, making it one of the most robust and accurate system.
BRIEF DESCRIPTION OF THE DRAWINGS
The illustrated embodiments of the subject matter will be understood by reference to the drawings, wherein like parts are designated by like numerals throughout. The following description is intended only by way of example, and simply illustrates certain selected embodiments of devices, systems, and methods that are consistent with the subject matter as claimed herein, wherein:
FIGURE 1: CONTENT-BASED IMAGE RETRIEVAL SYSTEM
FIGURE 2: PROPOSED SYSTEM USING FEATURE RE-WEIGHTING
DETAILED DESCRIPTION OF THE INVENTION
The proposed CBIR system operates through a sequence of processes, ensuring optimal feature extraction and retrieval accuracy. The core components of the system are as follows:
The preprocessing module standardizes image input by performing augmentation and normalization. Augmentation techniques include rotation, flipping, and scaling to improve model generalization, while normalization ensures consistency in pixel values. Noise removal further refines image quality, enabling more precise feature extraction.
The U-Net-based feature extraction module processes input images through an encoder-decoder structure. The encoder extracts hierarchical image features through convolutional layers, while the decoder reconstructs spatial information to retain fine details. This architecture ensures robust feature representation, making it well-suited for capturing subtle image variations.
The adaptive feature re-weighting mechanism refines feature importance by evaluating feature relevance using cosine similarity. The re-weighting process dynamically assigns higher importance to features that exhibit greater similarity to the query image. This context-aware adjustment enhances retrieval accuracy by focusing on the most discriminative features.
The feature indexing and retrieval module organizes extracted features into a structured database for efficient retrieval. Indexed features are compared against query features using cosine similarity, ranking retrieved images based on similarity scores. This ensures that the most relevant images appear at the top of the retrieval results.
The system interface module provides an intuitive platform for users to input query images and view retrieval results. The interface supports real-time feedback, allowing users to refine search results based on relevance feedback. This interactive component further improves retrieval performance.
By integrating these components, the proposed CBIR system achieves superior retrieval accuracy and scalability, making it an ideal solution for diverse image retrieval applications.
The algorithm for the proposed system is as follows
CBIR is intended to improve the correctness and competence of image retrieval systems by leveraging U-Net for feature extraction and implementing a feature re-weighting mechanism. The algorithm comprises several sequential steps: image preprocessing, feature mining using U-Net, feature re-weighting, and image retrieval. Below is a detailed outline of the CBIR algorithm.
Step 1: Input Image Collection
Image Dataset: Gather a shade of pictures which is helpful in two ways: Training and testing the model. Dataset: The data should be varied and multiple categories to make sure retrieval works best.
Step 2: Image Preprocessing
Normalization: Resize all the images to standard format and pixel range. Resize images to H × W and pixel values normalized in [0, 1]
I_"nrm " (x,y)=(I(x,y)-μ)/σ
Augmentation: Augment the training dataset using augmentation methods such as rotation, flipping, and scaling.
Step 3: Feature Extraction with U-Net
U-Net Architecture: Preprocessed images are passed through the U-Net framework for feature extraction.
Encoding Path: ReLU activation after convolutional layers and maximum poolings. The feature maps can be visualized as:
f_i=Relu⁡(W_i*I+b_i )
Decoding Path: Upsampling→ Concat with skip connections → conv 2:
f_"out " =Cnt⁡(U(f_i ),f_(i-1) )
Output Features: For each retinal OCT image, the last feature representation from U-Net was collected:
F_(U-Net)=f_"out "
Step 4: Feature Re-Weighting
Query Image Feature mining: Extract features from the inquiry image by means of the same U-Net architecture.
Cosine Resemblance Calculation: Calculate the cosine similarity between the query features Fq and the features of each DB image Fr

Cosine⁡(F_q,F_r )=(F_q⋅F_r)/∥F_q ∥∥F_r ∥
Weight Calculation: Determine the weight for each feature based on its relevance and user feedback:
For images marked as relevant, increase their feature weights:
W_i=" Base Weight "+" Feedback Score "⋅Cosine⁡(F_q,F_i )
For images marked as irrelevant, decrease their feature weights:
W_i=" Base Weight "-" Penalty Score "⋅Cosine(F_q,F_i )
Normalize weights: normalize the weights to ensure their sum to one:
W_i^'=W_i/(∑_(j=1)^N▒  W_j )
Weighted Feature Representation: Create the weighted feature representation:
F_"weighted " =∑_(i=1)^N▒  W_i^'⋅F_i
Step 5. Image Retrieval:
Distance Metric Calculation: Associating the weighted features of the query image through the weighted features of each image in the database using a distance metric:
D(F_"weighted,query " ,F_"weighted,database " )=√(∑_(k=1)^M▒  (F_("weighted,query," ,k)-F_("weighted,database " ,k) )^2 )
Ranking: rank the database images founded on their distance score and select the top K images:
R=Rank(D(F_"weighted,query " ,F_"weighted,database " ))
Step 6: Output: Return the top n images as a result from retrieval process
, Claims:1. A content-based image retrieval system comprising:
A preprocessing module configured to perform image augmentation, normalization, and noise reduction to standardize image input.
A feature extraction module utilizing a U-Net model to extract hierarchical image features, preserving both local and global contextual information.
An adaptive feature re-weighting mechanism that dynamically adjusts feature importance based on cosine similarity between query and database images.
A feature indexing and retrieval module configured to compare extracted features with stored database features and rank retrieved images based on similarity scores.
A user interface module providing real-time query input, retrieval visualization, and feedback-based refinement for improved search results.
2. The system as claimed in claim 1, wherein the preprocessing module enhances image quality through augmentation techniques including rotation, flipping, and scaling.
3. The system as claimed in claim 1, wherein the U-Net-based feature extraction module employs an encoder-decoder structure to retain spatial hierarchies and capture discriminative features.
4. The system as claimed in claim 1, wherein the adaptive feature re-weighting mechanism prioritizes features using cosine similarity to improve retrieval relevance.
5. The system as claimed in claim 1, wherein the indexing module organizes extracted features using a structured database to facilitate efficient search and retrieval.
6. The system as claimed in claim 1, wherein the retrieval module ranks images based on similarity scores computed from feature comparisons.
7.The system as claimed in claim 1, wherein the interface module allows users to refine search results through real-time feedback, improving retrieval precision.
8. The system as claimed in claim 1, wherein the system supports medical image retrieval applications by optimizing feature extraction for medical image datasets.
9. The system as claimed in claim 1, wherein the system is adaptable to general-purpose image retrieval tasks, ensuring high retrieval accuracy across diverse domains.
10. The system as claimed in claim 1, wherein the system is designed for scalability, enabling retrieval from large-scale image databases while maintaining computational efficiency.