K-NN Re-Ranking for Spatially-Constrained Object Retrieval
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Solution Overview
Problem
Conventional object retrieval techniques using the bag-of-words model lack spatial information and suffer from degradation due to feature quantization and intrinsic limitations in handling large variations in object appearance, leading to incomplete search results.
Innovation Solution
The implementation of a k-nearest neighbor (k-NN) re-ranking method that leverages spatially-constrained similarity measures and voting-based scoring techniques to improve search results by considering spatial consistency and handling object transformations like rotation, translation, and deformation, while discarding similarities between images to avoid degradation from wrong neighbors.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If bag-of-words model is used for image retrieval, then indexing efficiency and feature presentation are improved, but spatial information is lost and retrieval accuracy deteriorates
Solution Approach 1:
The patent segments the retrieval process into two distinct stages: (1) initial fast retrieval using the bag-of-words model for efficient indexing, and (2) subsequent refinement using spatial constraints and k-NN re-ranking. This segmentation allows the system to benefit from both the efficiency of histogram-based indexing and the accuracy of spatial-aware methods without compromising either.
2Speed
If feature quantization is applied in bag-of-words model, then indexing speed is improved, but feature discriminative power deteriorates
Solution Approach 1:
The patent performs preliminary feature quantization to enable fast indexing, then compensates for the resulting information loss through subsequent spatial constraint verification and k-NN re-ranking operations. This preliminary action followed by refinement allows the system to achieve both fast indexing and maintained feature discriminative power.
3Adaptability or versatility
If conventional query expansion is used to handle object variations, then retrieval coverage is improved, but performance deteriorates due to wrong neighbors
Solution Approach 1:
The patent implements a feedback mechanism through iterative k-NN re-ranking where the results from each iteration inform the next. By using the retrieved neighbors to refine the query and re-rank results, the system continuously improves retrieval accuracy while filtering out wrong neighbors, thus maintaining both high retrieval coverage and robust performance.
Solution Approach 2:
The patent introduces spatial constraints and k-NN re-ranking as intermediary mechanisms between the initial bag-of-words retrieval and the final results. These intermediaries filter out incorrect matches and refine the query representation, enabling the system to handle object variations reliably without being degraded by wrong neighbors.
4Measurement precision
If spatial constraints are strictly enforced in bag-of-words model, then retrieval accuracy for specific cases is improved, but versatility deteriorates
Solution Approach 1:
The patent applies spatial constraints dynamically rather than statically. The degree of spatial constraint enforcement is adjusted based on the retrieval context and results, allowing the system to maintain high accuracy for cases where spatial information is reliable while remaining versatile and adaptable to cases where spatial constraints may not be applicable or may introduce errors.
Data Source
AI summary
Methods, apparatus, and computer-readable storage media for k-NN re-ranking. Based on retrieved images and localized objects, a k-NN re-ranking method may use the k-nearest neighbors of a query to refine query results. Given the top k retrieved images and their localized objects, each k-NN object may be used as a query to perform a search. A database image may have different ranks when using those k-nearest neighbors as queries. Accordingly, a new score for each database image may be collaboratively determined by those ranks, and re-ranking may be performed using the new scores to improve the search results. The k-NN re-ranking technique may be performed two or more times, each time on a new set of k-nearest neighbors, to further refine the search results.