Iterative Feature Representation for Subject Identification
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Solution Overview
Problem
Object re-identification in computer vision faces challenges due to variations in image perspective and subject pose, leading to low similarity scores and difficulties in identifying unknown subjects across different camera views and times.
Innovation Solution
An iterative attentional aggregation model is employed to update feature representations of query images based on similarity scores with gallery images, leveraging multiple images to improve identification confidence by iteratively merging and updating feature representations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional feature matching is used between query image and gallery images, then the identification process is simple and fast, but the similarity scores are low and identification accuracy deteriorates due to variations in perspective and pose
Solution Approach 1:
The patent applies dynamics by making the feature representation process adaptive and iterative rather than static. The system dynamically updates query feature representations based on similarity scores from multiple gallery images, allowing the feature extraction to evolve and improve throughout the identification process, thereby resolving the contradiction between accuracy and complexity.
Solution Approach 2:
The patent introduces an intermediary mechanism in the form of similarity scores that mediate between the query image and gallery images. These similarity scores serve as intermediate representations that guide the iterative updating of query features, enabling accurate identification while managing computational complexity through a structured intermediate processing stage.
2Reliability
If multiple gallery images are used to improve identification confidence, then the reliability of subject identification improves, but the computational time and processing complexity increase
Solution Approach 1:
The patent applies preliminary action by pre-computing and storing feature representations for all gallery images before the actual identification query is processed. This preliminary preparation allows the system to quickly retrieve and compare features during the identification process, reducing real-time processing time while maintaining high reliability through multiple gallery image comparisons.
Solution Approach 2:
The patent implements continuity of useful action through its iterative process where the system continuously refines query feature representations by incorporating information from multiple gallery images. This continuous updating process maintains high identification confidence while optimizing processing efficiency by building upon previous computational results rather than starting from scratch.
3Measurement precision
If iterative updating of feature representations is performed to improve matching accuracy, then the precision of subject identification improves, but the computational complexity and processing steps increase
Solution Approach 1:
The patent applies feedback by using similarity scores computed from gallery image comparisons to feed back into the query feature representation updating process. This feedback loop allows the system to iteratively improve matching accuracy by adjusting query features based on actual performance against gallery images, resolving the contradiction between precision and complexity through intelligent feedback-driven optimization.
Data Source
AI summary
A computer-vision method includes recognizing a feature representation of a query image depicting an unknown subject. A similarity score is computed between the representation of the query image and feature representations of a plurality of gallery images collectively depicting two or more different subjects with at least two or more gallery images for each subject, and each gallery image having a label identifying which of the subjects is depicted. One or more updated feature representations of the query image are sequentially iterated based on one or more of the computed similarity scores. For each of the one or more updated feature representations, an updated similarity score is computed between the updated feature representation and the feature representations of each of the gallery images. The unknown subject is identified based on a gallery image having a highest updated similarity score.