Deep Feature Ranking for Person Re-Identification

Abstract

Person re-identification plays a critical part in many surveillance applications. Due to complicated illumination environments and various viewpoints, it is still a challenging problem to extract robust features. To solve this issue, we propose a novel deep feature ranking scheme. Our main contribution is to rank achieved deep features, which are obtained by classic deep learning model, and set the sort order number as our feature vector, named as ordinal deep features (ODFs). Person re-identification results are acquired by ranking person candidates by measuring distance based on ODFs. Since applying for rank orders rather than original feature values, our method achieves robust results, especially under the situation of viewpoints shift. Comprehensive experiments are carried out to demonstrate the significance of the proposed feature. Meanwhile, comparative experiments are applied over the publicly available dataset, our method achieves promising performance and outperforms the state of the art methods. Moreover, we applied the proposed feature in the scenario of image classification and discussed the effectiveness. INDEX TERMS Ordinal deep features, person re-identification, deep neural network, video surveillance.

HAR_Locator: a novel protein subcellular location prediction model of immunohistochemistry images based on hybrid attention modules and residual units

“…With the evolution of deep learning, predictors based on Convolutional Neural Networks (CNNs) map image feature vectors into high-dimensional space through numerous nonlinear activation functions to obtain more robust representations and produce impressive performance in many fields, such as Face Recognition, Image Recognition, and Object Detection ( Szegedy et al, 2013 ; Simonyan and Zisserman, 2014 ; Sun et al, 2015 ). Meanwhile, various abstract features from classical deep learning models trained in a fully supervised setting have consistently proved effective on generic vision tasks, such as Person Re-identification and Human Activity Recognition ( Donahue et al, 2014 ; Sani et al, 2017 ; Nie et al, 2019 ). Consequently, feature maps in the last or penultimate layer of pre-train CNNs were extracted and incorporated into shallow features to enhance the supervisory and distinctness of protein subcellular location in the IHC images ( Shao et al, 2017 ; Liu et al, 2019 ; Xue et al, 2020 ; Su et al, 2021 ; Ullah et al, 2021 ).…”