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Deep Representation Learning with Part Loss for Person Re-Identification
- Publication Year :
- 2017
-
Abstract
- Learning discriminative representations for unseen person images is critical for person Re-Identification (ReID). Most of current approaches learn deep representations in classification tasks, which essentially minimize the empirical classification risk on the training set. As shown in our experiments, such representations commonly focus on several body parts discriminative to the training set, rather than the entire human body. Inspired by the structural risk minimization principle in SVM, we revise the traditional deep representation learning procedure to minimize both the empirical classification risk and the representation learning risk. The representation learning risk is evaluated by the proposed part loss, which automatically generates several parts for an image, and computes the person classification loss on each part separately. Compared with traditional global classification loss, simultaneously considering multiple part loss enforces the deep network to focus on the entire human body and learn discriminative representations for different parts. Experimental results on three datasets, i.e., Market1501, CUHK03, VIPeR, show that our representation outperforms the existing deep representations.<br />9 pages, 9 figures
- Subjects :
- FOS: Computer and information sciences
Training set
Computer science
business.industry
Computer Vision and Pattern Recognition (cs.CV)
Reliability (computer networking)
Feature extraction
Representation (systemics)
Computer Science - Computer Vision and Pattern Recognition
02 engineering and technology
Machine learning
computer.software_genre
Computer Graphics and Computer-Aided Design
Convolutional neural network
ComputingMethodologies_PATTERNRECOGNITION
Discriminative model
0202 electrical engineering, electronic engineering, information engineering
020201 artificial intelligence & image processing
Artificial intelligence
business
Feature learning
computer
Software
Subjects
Details
- Language :
- English
- Database :
- OpenAIRE
- Accession number :
- edsair.doi.dedup.....1634965a75e2a4c3bc6f04229981b812