Local Descriptor Quality Scoring for Object Recognition
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Solution Overview
Problem
Current visual object recognition approaches using local descriptors often result in false matches and computational overhead due to common local structures being matched across different objects, and some descriptors never participate in matches, affecting recognition performance.
Innovation Solution
A system and method that estimate the performance of local descriptors by calculating a quality score based on the number of nearest neighbor descriptors related to images showing the object, allowing for the evaluation and potential discarding of ineffective descriptors to improve recognition efficiency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If local descriptors are extracted at specific locations in the image by interest point detectors, then object recognition can be performed using these descriptors, but false matches occur when common local structures are matched across different objects
Solution Approach 1:
The patent changes the parameter of descriptor selection by using quality scores to filter descriptors. Instead of using all extracted descriptors, the system selects only those with quality scores above a threshold, thereby reducing false matches while maintaining recognition accuracy.
Solution Approach 2:
The patent applies local quality assessment by evaluating each local descriptor's quality score individually. Descriptors are assessed based on their specific characteristics (e.g., Hessian eigenvalue ratios, blobness scores) and only high-quality descriptors are retained, allowing different regions of the image to contribute differently to recognition.
2Productivity
If all local descriptors are used for object recognition, then comprehensive coverage is achieved, but computational and memory overhead increases due to descriptors that never participate in matches
Solution Approach 1:
The patent extracts and removes ineffective descriptors from the set of all local descriptors. By pre-computing quality scores and filtering out descriptors with scores below a threshold, the system eliminates descriptors that would never participate in matches, thereby reducing computational overhead while maintaining recognition effectiveness.
Solution Approach 2:
The patent performs preliminary action by pre-evaluating descriptor quality before the actual recognition process. Quality scores are computed in advance and used to filter descriptors, so that during recognition only the most promising descriptors need to be processed, significantly reducing real-time computational requirements.
3Stability of the object's composition
If local descriptors with common local structures are used, then robustness to illumination variations is improved, but false matches increase when identifying objects in images
Solution Approach 1:
The patent changes the selection criterion by introducing quality scores that balance robustness and specificity. Descriptors are retained only if their quality score exceeds a threshold, which filters out descriptors that are too generic (causing false matches) while keeping those that are sufficiently robust to illumination variations.
Solution Approach 2:
The patent assesses the local quality of each descriptor individually using metrics such as Hessian eigenvalue ratios and blobness scores. This allows the system to keep descriptors that are robust to illumination changes but discard those that are too generic, achieving a balance between stability and match accuracy.
Data Source
AI summary
Systems and methods for selecting interest point descriptors for object recognition. In an embodiment, the present invention estimates performance of local descriptors by (1) receiving a local descriptor relating to an object in a first image; (2) identifying one or more nearest neighbor descriptors relating to one or more images different from the first image, the nearest neighbor descriptors comprising nearest neighbors of the local descriptor; (3) calculating a quality score for the local descriptor based on the number of nearest neighbor descriptors that relate to images showing the object; and (4) determining, on the basis of the quality score, if the local descriptor is effective in identifying the object.