Feature Based Image Retrieval Using Region Specific Encoders
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Solution Overview
Problem
Conventional image retrieval methods fail to effectively match and retrieve specific features within images, as they rely on entire image analysis rather than focusing on particular features like regions of interest, making it challenging to identify and isolate important features for accurate retrieval.
Innovation Solution
A method and system for feature-based image retrieval that selects a feature-specific encoder based on a Region of Interest (ROI) using a mask-based loss function, computes feature vectors, and employs Locality Sensitive Hashing (LSH) to compare and retrieve images with similar features from a database, allowing for precise matching of specific image features.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional methods utilize the entire image for image retrieval, then the retrieval process is simple, but it fails to match specific features within the image
Solution Approach 1:
The patent divides the image into multiple regions of interest (ROIs) and creates separate feature encoders for each region. This segmentation allows the system to focus on specific features (e.g., nose, eyes, cars) independently, improving feature matching accuracy while managing complexity through modular encoder design.
Solution Approach 2:
The patent extracts specific regions of interest from the entire image using masks and focuses computation only on these extracted regions. By taking out only the relevant features (e.g., extracting the nose region from a face image), the system achieves precise feature matching without processing the entire image, thus improving accuracy while controlling complexity.
2Measurement precision
If conventional methods perform image retrieval based on keywords or metadata, then the process is fast, but it cannot identify and disentangle important features of the image
Solution Approach 1:
The patent performs preliminary action by pre-training feature-specific encoders for different regions of interest before the actual retrieval process. These pre-trained encoders are stored and can be quickly applied during retrieval, allowing the system to identify important features accurately without time-consuming processing during the retrieval phase itself.
Solution Approach 2:
The patent creates multiple feature-specific encoders that are copies of the base encoder architecture, each specialized for a particular region or feature type. These copied encoders enable parallel processing of different features, maintaining fast retrieval speeds while achieving precise feature identification through specialization.
3Measurement precision
If the system uses multiple feature specific encoders for different regions, then feature matching accuracy improves, but computational complexity increases
Solution Approach 1:
The patent designs a universal base encoder architecture that can be adapted to multiple regions through parameter sharing and pre-training. This multi-functional encoder design allows the same underlying structure to handle different features (faces, cars, objects) by loading appropriate pre-trained weights, reducing overall system complexity while maintaining high feature retrieval accuracy.
Data Source
AI summary
Image Retrieval is an application of computer vision that deals with searching images in large databases. Conventional methods utilize the entire image to perform the image retrieval task rather than considering specific features. The embodiments herein provide a method and system for feature based image retrieval. Initially, the system receives an input image and a query label. Further, a feature specific encoder is selected from a plurality of feature specific encoders based on the query label. A first set of feature vectors are computed from the input image using the selected feature specific encoder. Further, a Locality Sensitive Hashing (LSH) value is computed from the first set of feature vectors. Finally, a plurality of matching images is obtained from a plurality database images based on a comparison between the computed feature specific LSH value and a plurality of feature specific LSH values stored in a feature specific LSH database.