Person reidentification by semisupervised dictionary rectification learning with retraining module Article Swipe

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Hongyuan Wang , Zongyuan Ding , Ji Zhang , Suolan Liu , Tongguang Ni , Fuhua Chen · YOU? · · 2018 · Open Access · · DOI: https://doi.org/10.1117/1.jei.27.4.043043

At present, in the field of person reidentification (re-id), the commonly used supervised learning algorithms require a large amount of labeled samples, which is not conducive to the model promotion. On the other hand, the accuracy of unsupervised learning algorithms is lower than supervised algorithms due to the lack of discriminant information. To address these issues, we make use of a small amount of labeled samples to add discriminant information in the basic dictionary learning. Moreover, the sparse coefficients of dictionary learning are decomposed into a projection problem of the original features, and the projection matrix is trained by labeled samples, which is transformed into a metric learning problem. It thus integrates the advantages of the two methods through combining dictionary learning and metric learning. After the data are trained, a projection matrix is used to project the unlabeled features into a feature subspace and the labels of the samples are reconstructed. The semisupervised learning problem is then transformed to a supervised learning problem with a graph regularization term. Experiments on different public pedestrian datasets, such as VIPeR, PRID, iLIDS, and CUHK01, show that the recognition accuracy of our method is better than some other existing person re-id methods.

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article

Language

en

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https://doi.org/10.1117/1.jei.27.4.043043

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https://www.spiedigitallibrary.org/journals/journal-of-electronic-imaging/volume-27/issue-4/043043/Person-reidentification-by-semisupervised-dictionary-rectification-learning-with-retraining-module/10.1117/1.JEI.27.4.043043.pdf

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hybrid

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3

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40

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10

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https://openalex.org/W2886908327

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https://openalex.org/W2886908327

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DOI

https://doi.org/10.1117/1.jei.27.4.043043

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Title

Person reidentification by semisupervised dictionary rectification learning with retraining module

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article

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Language

en

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Publication year

2018

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2018-08-11

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Authors

Hongyuan Wang, Zongyuan Ding, Ji Zhang, Suolan Liu, Tongguang Ni, Fuhua Chen

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https://doi.org/10.1117/1.jei.27.4.043043

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https://www.spiedigitallibrary.org/journals/journal-of-electronic-imaging/volume-27/issue-4/043043/Person-reidentification-by-semisupervised-dictionary-rectification-learning-with-retraining-module/10.1117/1.JEI.27.4.043043.pdf

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hybrid

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https://www.spiedigitallibrary.org/journals/journal-of-electronic-imaging/volume-27/issue-4/043043/Person-reidentification-by-semisupervised-dictionary-rectification-learning-with-retraining-module/10.1117/1.JEI.27.4.043043.pdf

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Concepts

Artificial intelligence, Semi-supervised learning, Computer science, Pattern recognition (psychology), Subspace topology, Discriminant, Machine learning, Dictionary learning, Metric (unit), Linear discriminant analysis, Supervised learning, Projection (relational algebra), Sparse approximation, Artificial neural network, Algorithm, Operations management, Economics

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Cited by

3

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2023: 3

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40

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Related works (count)

10

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