Bag of local features for person re-identification on large-scale datasets

Yixiu Liu; Yunzhou Zhang; Jianning Chi

doi:10.1117/1.JEI.27.5.053041

17 October 2018 Bag of local features for person re-identification on large-scale datasets

Yixiu Liu, Yunzhou Zhang, Jianning Chi

Author Affiliations +

Journal of Electronic Imaging, Vol. 27, Issue 5, 053041 (October 2018). https://doi.org/10.1117/1.JEI.27.5.053041

Abstract

In recent years, large-scale person re-identification has attracted a lot of attention from video surveillance. Usual approaches addressing this task either learn the effective feature embeddings or design the learning architectures to obtain discriminative metrics. Most of them only focus on improving the accuracy of recognition but neglect retrieval efficiency. To improve the accuracy and efficiency of person re-identification simultaneously, an accurate and fast method is proposed based on the bag of visual words (BoVW) model, which has widely been applied in image retrieval. A bag of local features is developed to simplify feature representation for person re-identification. Cross-view dictionary learning is used to eliminate the redundancy of these local features. These local features consist of scale invariant feature transform and local maximal occurrence representation (LOMO) that are invariant in scale and color, respectively. Finally, integrated BoVW histograms are obtained, which encode the images by k-means clustering. Experiments conducted on the CUHK03, Market1501, and MARS datasets show that the proposed method performs favorably against existing approaches.

Citation Download Citation

Yixiu Liu, Yunzhou Zhang, and Jianning Chi "Bag of local features for person re-identification on large-scale datasets," Journal of Electronic Imaging 27(5), 053041 (17 October 2018). https://doi.org/10.1117/1.JEI.27.5.053041

Received: 15 April 2018; Accepted: 20 September 2018; Published: 17 October 2018

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