Low-rank representation (LRR) has been successfully applied to subspace clustering. However, the nuclear norm in the standard LRR is not optimal for approximating the rank function in many real-world applications. Meanwhile, the L21 norm in LRR also fails to characterize various noises properly. To address the above issues, we propose an improved LRR method, which achieves low rank property via the new formulation with weighted Schatten-p norm and Lq norm (WSPQ). Specifically, the nuclear norm is generalized to be the Schatten-p norm and different weights are assigned to the singular values, and thus it can approximate the rank function more accurately. In addition, Lq norm is further incorporated into WSPQ to model different noises and improve the robustness. An efficient algorithm based on the inexact augmented Lagrange multiplier method is designed for the formulated problem. Extensive experiments on face clustering and motion segmentation clearly demonstrate the superiority of the proposed WSPQ over several state-of-the-art methods.
Concept factorization (CF), as a popular matrix factorization technique, has recently attracted increasing attention in image clustering, due to the strong ability of dimension reduction and data representation. Existing CF variants only consider the local structure of data, but ignore the global structure information embedded in data, which is very crucial for data representation. To address the above issue, we propose an improved CF method, namely local and global regularized concept factorization (LGCF), by considering the local and global structures simultaneously. Specifically, the local geometric structure is depicted in LGCF via a hypergraph, which is capable of precisely capturing high-order geometrical information. In addition, to discover the global structure, we establish an unsupervised discriminant criterion, which characterizes the between-class scatter and the total scatter of the data with the help of latent features in LGCF. For the formulated LGCF, a multiplicative update rule is developed, and the convergence is rigorously proved. Extensive experiments on several real image datasets demonstrate the superiority of the proposed method over the state-of-the-art methods in terms of clustering accuracy and mutual information.
KEYWORDS: Databases, Fuzzy logic, Associative arrays, Statistical analysis, Principal component analysis, Image classification, Error control coding, Feature extraction, Detection and tracking algorithms, Chemical species
Representation-based classification methods, such as sparse representation-based classification, have been a breakthrough for face recognition recently. Under such a philosophy, we develop a framework that fuses virtual synthesized training samples as bases and fuzzy discriminant sparse residuals measurement in face classification. More specifically, the preprocessing of the proposed algorithm aims to alleviate sampling uncertainty by introducing extra training samples and then the features are extracted over the above dictionary using a hierarchical multiscale local binary patterns scheme. The second stage tries to approximate testing samples by a subset of training samples, and the introduced sparse coding process with weak l1-constraint has superior competitiveness in that the accuracy has been improved while the complexity has fallen. The third stage again determines a new weighted sum for the remaining informative samples. Hence, fuzzy sparse similarity grades are designed by the new weighted value, which can be merged into the typical discriminant analysis criterion. Experimental results from various benchmark face databases have demonstrated the effectiveness of our algorithm.
Calibration of the x-ray apparatus is necessary for many applications. Most of the literature on the calibration of x-ray apparatus seem to ignore the imaging deformation. Our main concern is how to apply Tsai's nonlinear camera calibration technique to the calibration of a medical x-ray apparatus with deformation. In order to achieve this goal, two key problems should be solved: The first is how to calibrate some key intrinsic parameters, namely, the imaging center and sampling step of the detector, which are usually provided by the camera manufacturer but are unknown for the x-ray apparatus. The second is how to model the serious imaging deformation. Some practical schema are designed to solve these two problems, and the whole calibration procedure and experimental results are presented.
Te paper proposed a new segmentation method, which is based on the relation stable-state. The relation stable-state is derived from the fact that the contour of an object may be enlarged or shrunk while the threshold is changing, but times that boundary points visited by contours is more than times that inner points visited by contours. This relation is usual stable. Minimum area and edge intensity are the only two parameters needed in it. Under the control of these two parameters, it chooses contours in the original image; sums them into a contour image; extracts contours in the contour image, does merge-split process and region growing step by step. In fact, it integrated gray value, edge information and space connectivity smartly. Experiments show it can be applied to extract objects with multi-level perfectly even if the image is non-uniform illumination, thus it is more general and practical.
The paper presents one effective method for ship recognition in the ship lock. The outdoors environment is very complexity in which there are shade, waves and speckles caused by the sunlight and wind or motion of ships. The accuracy of recognition is depended on the accuracy of disturb area detection. It analyzes their characters on gray level and structure, proposes a new method to form a special histogram of only those pixels besides the boundary. This histogram is fit for small object segment and also large. At the end, the features for recognition based on statistic are presented. The long time running in the temporary ship lock of Three Gorges Project proves the error rate of judging is less than three thousandth just using the statistic features and less than one over ten thousands cooperating with the others.
One of the most important aspects in the navigation of ALV is computer vision or machine vision. Usually, it is achieved by using multisensor fusion technology. As we know, laser radar is a typical sensor in this project, especially in the situation that there is an obstacle in a road. It is often effective to describe the relationship between road and obstacle by using height matrix from range data. However, when the front view is more complicated, such as a wall or a building on which exists a hole or a corridor big enough for ALV to go through, the above method may not be well done. For this reason, we propose a novel approach by using two matrixes from the range data to solve the problem. The main idea is that from the range data we figure out two matrixes, one is the height matrix, representing the height of the object from the horizontal plane, the other is the depth matrix representing the depth of the object from the laser radar vertical plane. By using the information of both height and depth, we can understand the front environment more precise and better.
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