Search

The deep learning based methods have improved the visual tracking precision significantly. However, the background distraction and the high precise localization remain challenging problems. Despite that some methods have fused the deep and shallow layer features to solve these problems, the existing fusion methods, like simply concatenating or adding the features from the different layers, cannot take the advantage of both the deep and shallow layer features fully. In this paper, we propose a new adaptive feature fusion method, called the instance-based feature pyramid (IBFP) to obtain the discriminative high-resolution feature, which not only inherits the discriminative information from the deep layer feature, but also keeps the high precision localization information of the shallow layer feature. For utilizing the deep and shallow features effectively, we design an instance-based upsampling (IBU) module to fuse them, and a compressed space channel selection (CSCS) module to re-weight the feature channels adaptively. We insert the IBU and CSCS modules in the Siamese tracker for end-to-end training and testing. By using the proposed IBU and CSCS modules, we fuse the deep and shallow features in a series manner. Experiments on large-scale benchmark datasets demonstrate that the proposed modules boost the capabilities of distinguishing the targets and the similar distractors and perform favorably against the state-of-the-art. [ABSTRACT FROM AUTHOR]

• We propose a joint image restoration and matching method to deal with the problem of blurred image matching. • The approach utilizes distance-weighted sparse representation prior to exploit the correlation between restoration and matching. • The approach applies a coarse to fine strategy to improve the matching accuracy and reduce the search time. Image matching is a classic problem in the field of image processing, aims to locate the unique position of the real-time images and has been widely used in visual-based navigation systems. However, most of the works on image matching simply assume the ideal inputs without considering the degradation of the real world, such as image blur. In the presence of such a situation, the traditional matching methods usually first resort to image restoration and then perform image matching with the restored image. However, by treating the restoration and matching separately, the accuracy of blurred image matching will be reduced by the defective output of the image restoration. In this paper, we propose a joint blurred image restoration and matching method based on distance-weighted sparse representation (JRM-DSR), which utilizes the sparse representation prior to exploit the correlation between restoration and matching. This prior assumes that the blurred image, if correctly restored, can be well represented as a sparse linear combination of the dictionary constructed by the reference image. In order to achieve more accurate matching results to help image restoration, we consider both local and sparse information as well as adopt distance-weighted sparse representation to obtain better representation coefficients. By iterative restoring the input image in pursuit of the sparest representation, our approach can achieve restoration and match simultaneous, and these two tasks can benefit greatly from each other. Moreover, a coarse-to-fine matching strategy is proposed to further improve the matching accuracy and search efficiency. Experiments demonstrate the effectiveness of our method compared with conventional methods. [ABSTRACT FROM AUTHOR]

The classification of hyperspectral image with a paucity of labeled samples is a challenging task. In this paper, we present a discriminant sparse representation (DSR) graph for semi-supervised learning (SSL) to address this problem. For graph-based methods, how to construct a graph among the pixels is the key to a successful classification. Our graph construction method contains two steps. Sparse representation (SR) method is first employed to estimate the probability matrix of the pairwise pixels belonging to the same class, and then this probability matrix is integrated into the SR graph, which can be obtained by solving an ℓ optimization problem, to form a DSR graph. Experiments on Hyperion and AVIRIS hyperspectral data show that our proposed method outperforms state of the art. [ABSTRACT FROM AUTHOR]