Conference proceeding
Exploiting Transitivity for Learning Person Re-identification Models on a Budget
2018 IEEE/CVF CONFERENCE ON COMPUTER VISION AND PATTERN RECOGNITION (CVPR), pp.7064-7072
IEEE Conference on Computer Vision and Pattern Recognition
01/01/2018
DOI: 10.1109/CVPR.2018.00738
Abstract
Minimization of labeling effort for person reidentification in camera networks is an important problem as most of the existing popular methods are supervised and they require large amount of manual annotations, acquiring which is a tedious job. In this work, we focus on this labeling effort minimization problem and approach it as a subset selection task where the objective is to select an optimal subset of image-pairs for labeling without compromising performance. Towards this goal, our proposed scheme first represents any camera network (with k number of cameras) as an edge weighted complete k-partite graph where each vertex denotes a person and similarity scores between persons are used as edge-weights. Then in the second stage, our algorithm selects an optimal subset of pairs by solving a triangle free subgraph maximization problem on the k-partite graph. This sub-graph weight maximization problem is NP-hard (at least for k >= 4) which means for large datasets the optimization problem becomes intractable. In order to make our framework scalable, we propose two polynomial time approximately-optimal algorithms. The first algorithm is a 1/2-approximation algorithm which runs in linear time in the number of edges. The second algorithm is a greedy algorithm with sub-quadratic (in number of edges) time-complexity. Experiments on three state-of-the-art datasets depict that the proposed approach requires on an average only 8-15% manually labeled pairs in order to achieve the performance when all the pairs are manually annotated.
Details
- Title: Subtitle
- Exploiting Transitivity for Learning Person Re-identification Models on a Budget
- Creators
- Sourya Roy - University of California, RiversideSujoy Paul - Univ Calif Riverside, Riverside, CA 92521 USANeal E. Young - University of California, RiversideAmit K. Roy-Chowdhury - University of California, Riverside
- Resource Type
- Conference proceeding
- Publication Details
- 2018 IEEE/CVF CONFERENCE ON COMPUTER VISION AND PATTERN RECOGNITION (CVPR), pp.7064-7072
- Publisher
- IEEE
- Series
- IEEE Conference on Computer Vision and Pattern Recognition
- DOI
- 10.1109/CVPR.2018.00738
- ISSN
- 1063-6919
- eISSN
- 2575-7075
- Number of pages
- 9
- Grant note
- IIS-1316934; IIS-1619463; CNS-1544969 / NSF; National Science Foundation (NSF)
- Language
- English
- Date published
- 01/01/2018
- Academic Unit
- Computer Science
- Record Identifier
- 9984446450802771
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