Image Re-ranking System Using Global and Local Feature Fusion
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Current image matching technologies face challenges in achieving robustness to changes in illumination, viewpoint, occlusion, and background clutter, particularly when handling large-scale datasets efficiently, and there is a need for real-time image matching systems that can operate on resource-constrained devices.
Innovation Solution
A system that combines global and local feature extractors with a filtering, fine-tuning, distance fusion, and confidence score generation modules to re-rank retrieved images, utilizing deep learning techniques such as CNNs and BRISK methods for efficient and accurate image matching.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If deep learning-based methods (CNNs, Siamese networks, triplet networks) are used for image matching, then accuracy and robustness to transformations are improved, but computational complexity and processing time increase
Solution Approach 1:
The system segments the image matching process into multiple stages: a first stage using CNN-based global feature extraction for initial retrieval, and a second stage using BRISK local features for fine-tuning and verification. This segmentation allows each stage to handle specific tasks efficiently, reducing overall computational complexity while maintaining high accuracy and robustness.
Solution Approach 2:
The system applies different feature extraction methods to different aspects of the images: global CNN features for overall image representation and local BRISK features for detailed pattern matching. This local quality approach enables the system to leverage the strengths of both deep learning and traditional methods without the full computational burden of deep learning alone.
2Productivity
If handcrafted feature extraction methods (SIFT, SURF) combined with RANSAC are used, then computational efficiency is improved, but robustness to image transformations deteriorates
Solution Approach 1:
The system merges handcrafted BRISK local feature extraction with deep learning-based global feature extraction. The BRISK features provide computational efficiency and geometric invariance, while the CNN global features provide robustness to illumination, viewpoint, and occlusion changes. This combination achieves both efficiency and robustness that neither method alone could provide.
Solution Approach 2:
The system creates a composite approach by combining multiple feature extraction techniques (CNN global features and BRISK local features) and multiple verification methods (geometric constraints, spatial verification, multi-modal matching). This composite material of algorithms achieves superior performance by leveraging the complementary strengths of each component.
3Ease of operation
If traditional image matching algorithms are used, then ease of operation is maintained, but measurement precision and manufacturing precision deteriorate
Solution Approach 1:
The system introduces an intermediary reranking module that mediates between the initial CNN-based retrieval results and the final matching output. This intermediary stage uses BRISK feature verification and geometric constraints to refine the matches, improving precision while maintaining the ease of operation through automated processing.
4Measurement precision
If large-scale image datasets are processed, then accuracy is improved, but processing time and computational resources increase
Solution Approach 1:
The system segments the processing into two stages: a fast initial retrieval stage using CNN global features and a more detailed verification stage using BRISK local features. This segmentation enables the system to process large-scale datasets efficiently by performing coarse filtering first and then applying more computationally intensive methods only to promising candidates.
Solution Approach 2:
The system applies partial action by using BRISK feature verification only on the top-k retrieved images rather than all images in the dataset. This selective verification approach maintains high accuracy on the most relevant matches while significantly reducing processing time compared to exhaustive verification.
Data Source
AI summary
A system for re-ranking retrieved matching images. The system comprises a processor configured to extract at least one QI global feature from query image by global feature extractor module, and extract plurality of QI local features for the query image by local feature extractor module, and then search and retrieve top-k reference images from plurality of reference images based on at least one QI global feature and at least one KRI global feature associated with top-k reference images by filtering module. The processor is configured to perform matching of the plurality of QI local features with the plurality of KRI local features associated with the top-k reference images by fine tuning module and generate matching distance by distance fusion module, wherein the top-k reference images are re-ranked based on the matching distance and generate confidence score for the top-n reference images by confidence score generation module.