Deep Adaptive Feature Embedding with Local Sample Distributions for Person Re-identification

Person re-identification (re-id) aims to match pedestrians observed by disjoint camera views. It attracts increasing attention in computer vision due to its importance to surveillance system. To combat the major challenge of cross-view visual variations, deep embedding approaches are proposed by learning a compact feature space from images such that the Euclidean distances correspond to their cross-view similarity metric. However, the global Euclidean distance cannot faithfully characterize the ideal similarity in a complex visual feature space because features of pedestrian images exhibit unknown distributions due to large variations in poses, illumination and occlusion. Moreover, intra-personal training samples within a local range are robust to guide deep embedding against uncontrolled variations, which however, cannot be captured by a global Euclidean distance. In this paper, we study the problem of person re-id by proposing a novel sampling to mine suitable \textit{positives} (i.e. intra-class) within a local range to improve the deep embedding in the context of large intra-class variations. Our method is capable of learning a deep similarity metric adaptive to local sample structure by minimizing each sample's local distances while propagating through the relationship between samples to attain the whole intra-class minimization. To this end, a novel objective function is proposed to jointly optimize similarity metric learning, local positive mining and robust deep embedding. This yields local discriminations by selecting local-ranged positive samples, and the learned features are robust to dramatic intra-class variations. Experiments on benchmarks show state-of-the-art results achieved by our method.Comment: Published on Pattern Recognitio

An Effective Data Embedding Technique Based on APPM in Transform Domain

This paper proposes an efficient data embedding technique based on adaptive pixel pair matching in transform domain. The basic principle of a Pixel Pair Matching (PPM) based data embedding technique is to use the values of a pixel pair as a reference coordinate and search a coordinate in the neighborhood set of that pixel pair according to given message digit. In order to conceal secret data the pixel pair is then replaced by the searched coordinate. In transform domain data embedding techniques, the image pixels are converted into transform domain by using a particular transform and then the secret data is embedded by using an efficient data embedding algorithm. In this paper the Haar transform is used. The proposed method not only offers lower embedding distortion but also more robust against various noise attacks. The experimental results shows that this method performs better when compared to the spatial domain technique