Sparse Feature Preservation for Relative Attribute Learning

Wu, Han; Kong, Xiangwei; Yang, Hongxue; Li, Ying

doi:10.1109/ism.2016.0085

Cited by 2 publications

(1 citation statement)

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“…Kim et al, Parikh and Grauman, and Souri et al proposed relative attributes for providing more descriptive information to the images. Ulteriorly, as the local features do not have much positive effects on learning global attributes, Wu et al introduced a sparse feature preservation (SFP) method to preserve the most important features on the learning of each attribute model.…”

Section: Introductionmentioning

confidence: 99%

Data‐driven pedestrian re‐identification based on hierarchical semantic representation

Cheng

Tao

et al. 2017

Concurrency and Computation

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Summary Limited number of labeled data of surveillance video causes the training of supervised model for pedestrian re‐identification to be a difficult task. Besides, applications of pedestrian re‐identification in pedestrian retrieving and criminal tracking are limited because of the lack of semantic representation. In this paper, a data‐driven pedestrian re‐identification model based on hierarchical semantic representation is proposed, extracting essential features with unsupervised deep learning model and enhancing the semantic representation of features with hierarchical mid‐level ‘attributes’. Firstly, CNNs, well‐trained with the training process of CAEs, is used to extract features of horizontal blocks segmented from unlabeled pedestrian images. Then, these features are input into corresponding attribute classifiers to judge whether the pedestrian has the attributes. Lastly, with a table of ‘attributes‐classes mapping relations’, final result can be calculated. Under the premise of improving the accuracy of attribute classifier, our qualitative results show its clear advantages over the CHUK02, VIPeR, and i‐LIDS data set. Our proposed method is proved to effectively solve the problem of dependency on labeled data and lack of semantic expression, and it also significantly outperforms the state‐of‐the‐art in terms of accuracy and semanteme.

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Section: Introductionmentioning

confidence: 99%