As a periodic movement, gait contains informative biometric traits formed by individual body structures, motion patterns, and behavioral habits. Previous gait recognition methods mainly focus on mining the appearance cues from gait sequences, while neglecting the dynamic motion characteristics. Motion cues are important complementary information for generating high-quality gait representations that can help models accurately recognize individuals. In this article, we propose a novel gait recognition framework named GaitDS to model dynamic motion information and construct salient gait representations. Specifically, we develop a motion information perception module that can directly represent dynamic regions during walking and extract fine-grained motion features based on the appearance of body parts over time. In addition, since some frames in gait sequences share partial similarities, we present saliency identity representation learning to focus on key frames along the temporal dimension, and integrate salient identity features to enhance sequence-level representations. Furthermore, a channel enhanced module is designed to generate more discriminative gait representations, where motion and temporal salient features can be complemented with global representations. Compared with existing state-of-the-art methods, our model achieves superior average rank-1 recognition accuracy on three benchmark datasets, i.e., 93.7% on CASIA-B, 92.4% on OU-MVLP, and 50.7% on Gait3D.

Crowd counting remains a challenging vision task due to the presence of several problems such as severe occlusions, perspective distortions and scale variations in the target scene. How to design an accurate and robust crowd counting estimator has attracted intensive research interest in the past few decades. It is well-known that learning rich features representation is crucial for crowd counting. However, the existing neural-networks-based methods only employ CNN features extracted from the last convolutional layer, and the useful hierarchical information contained in the CNN features is overlooked. To address this problem, we propose a CNN architecture based on the fully convolutional network, which is used to build an end-to-end density map estimation system by combining some of the meaningful convolutional features. Such a combination is exploited to effectively capture both the multi-scale and the multi-level information in complex scenes. Extensive experiments on most existing crowd counting dataset- s including ShanghaiTech Part A, ShanghaiTech Part B and UCF CC 50 demonstrate the effectiveness and the reliability of our approach.

As a promising alternative to traditional search techniques, hashing‐based approximate nearest neighbour search provides an applicable solution for big data. Most existing efforts are devoted to finding better projections to preserve the neighbouring structure of original data points in Hamming space, but ignore the quantisation procedure which may lead to the breakdown of the neighbouring structure maintained in the projection stage. To address this issue, the authors propose a novel multi‐bit quantisation (MBQ) method using a Matthews correlation coefficient (MCC) term and a regularisation term. The authors' method utilises the neighbouring relationship and the distribution information of original data points instead of the projection dimension usually used in the previous MBQ methods to adaptively learn optimal quantisation thresholds, and allocates multiple bits per projection dimension in terms of the learned thresholds. Experiments on two typical image data sets demonstrate that the proposed method effectively preserves the similarity between data points in the original feature space and outperforms state‐of‐the‐art quantisation methods.

Band selection is an important preprocessing technique for hyperspectral imagery, through which a subset of critical and representative spectral bands can be selected from a raw image cube for compact yet effect representation. Among the valid selection strategies, performing band selection in an unsupervised manner is usually considered more general due to its application-independent characteristic. This letter proposed a novel unsupervised hyperspectral band selector that can capture the interband redundancy nature of hyperspectral images through low-rank modeling. Experiments on three real-world hyperspectral data sets demonstrated that the proposed band selector can generate band subsets suitable in the context of hyperspectral pixel classification.

This study presents a local image descriptor based on spectral embedding. Specifically, the spectra of line graph are used to represent image edges, corners and edge points with big curvature. The authors theoretically analyse and experimentally verify that the spectra of line graph are robust to noise and are invariant to rotation and linear intensity changes. Based on such a fact, some local image descriptors are constructed using the spectra of line graph. Comparative experiments demonstrate the effectiveness of the proposed descriptor and its superiority to some state‐of‐the‐art descriptors under image rotation, image blur, viewpoint change, illumination change, JPEG compression and noise.