Saliency preprocessing for person re-identification images

Abstract

In this paper, we propose a preprocessing strategy for refining the pedestrian images in person re-identification(re-id). The accomplishment of the person re-id task depends on the features extracted from pedestrian appearances. The best image matches are verified based on these features as identification results. The saliency information in image scenes is often exploited for feature selection in high-level vision tasks. Inspired by the pre-attentive mechanism in human visual system, we utilize the saliency information in the re-identification data to refine the person appearance in a preprocessing step. We first obtain the eye-fixation-predicting map based on the saliency analysis of image. Then this map is used to spatially weight the image features for a better appearance. Finally, we apply these processed images to the feature extraction in a standard re-identification procedure. Experiments on the widely-used VIPeR dataset show that the proposed method improves the final performance of re-identification task.