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Color invariants for person reidentification
Igor Kviatkovsky1, Amit Adam, Ehud Rivlin
1Department of Computer Science, Technion-Israel Institute of Technology, Technion City, Haifa 32000, Israel. kviat@cs.technion.ac.il
This study introduces a new method for object recognition using color distributions. The novel intradistribution structure is invariant to lighting changes, improving person identification accuracy.
Area of Science:
- Computer Vision
- Pattern Recognition
- Image Analysis
Background:
- Specific object recognition, particularly person identification, often relies on color distributions.
- Color distributions can be multimodal and possess inherent structures.
- These structures change under varying illumination but retain invariant aspects.
Purpose of the Study:
- To develop a robust object recognition method using color distributions.
- To identify and leverage invariant structures within color distributions under changing lighting conditions.
- To enhance the accuracy of specific person identification.
Main Methods:
- Utilizing an "intradistribution structure" invariant to illumination changes.
- Employing shape context descriptors to represent this intradistribution structure.
- Validating invariance under illumination changes using a diagonal model.
Main Results:
- Color information alone achieved good recognition performance on diverse indoor and outdoor databases.
- The proposed signature demonstrated invariance to specific illumination variations.
- Combining the approach with covariance descriptors surpassed state-of-the-art results on VIPeR and CAVIAR4REID.
Conclusions:
- The intradistribution structure derived from color distributions offers a practical and invariant feature for object recognition.
- This method shows significant promise for person identification and other recognition tasks.
- The approach achieves superior performance, outperforming existing state-of-the-art methods.
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