Chunhong Pan

Discriminative Least Squares Regression for Multiclass Classification and Feature Selection

This paper presents a framework of discriminative least squares regression (LSR) for multiclass classification and feature selection. The core idea is to enlarge the distance between different classes under the conceptual framework of LSR. First, a technique called ε-dragging is introduced to force the regression targets of different classes moving along opposite directions such that the distances between classes can be enlarged. Then, the ε-draggings are integrated into the LSR model for multiclass classification. Our learning framework, referred to as discriminative LSR, has a compact model form, where there is no need to train two-class machines that are independent of each other. With its compact form, this model can be naturally extended for feature selection. This goal is achieved in terms of L2,1 norm of matrix, generating a sparse learning model for feature selection. The model for multiclass classification and its extension for feature selection are finally solved elegantly and efficiently. Experimental evaluation over a range of benchmark datasets indicates the validity of our method.

A Graph-Based Classification Method for Hyperspectral Images

The goal of this paper is to apply graph cut (GC) theory to the classification of hyperspectral remote sensing images. The task is formulated as a labeling problem on Markov random field (MRF) constructed on the image grid, and GC algorithm is employed to solve this task. In general, a large number of user interactive strikes are necessary to obtain satisfactory segmentation results. Due to the spatial variability of spectral signatures, however, hyperspectral remote sensing images often contain many tiny regions. Labeling all these tiny regions usually needs expensive human labor. To overcome this difficulty, a pixelwise fuzzy classification based on support vector machine (SVM) is first applied. As a result, only pixels with high probabilities are preserved as labeled ones. This generates a pseudouser strike map. This map is then employed for GC to evaluate the truthful likelihoods of class labels and propagate them to the MRF. To evaluate the robustness of our method, we have tested our method on both large and small training sets. Additionally, comparisons are made between the results of SVM, SVM with stacking neighboring vectors, SVM with morphological preprocessing, extraction and classification of homogeneous objects, and our method. Comparative experimental results demonstrate the validity of our method.

Regression Reformulations of LLE and LTSA With Locally Linear Transformation

Locally linear embedding (LLE) and local tangent space alignment (LTSA) are two fundamental algorithms in manifold learning. Both LLE and LTSA employ linear methods to achieve their goals but with different motivations and formulations. LLE is developed by locally linear reconstructions in both high- and low-dimensional spaces, while LTSA is developed with the combinations of tangent space projections and locally linear alignments. This paper gives the regression reformulations of the LLE and LTSA algorithms in terms of locally linear transformations. The reformulations can help us to bridge them together, with which both of them can be addressed into a unified framework. Under this framework, the connections and differences between LLE and LTSA are explained. Illuminated by the connections and differences, an improved LLE algorithm is presented in this paper. Our algorithm learns the manifold in way of LLE but can significantly improve the performance. Experiments are conducted to illustrate this fact.

TurboPixel Segmentation Using Eigen-Images

TurboPixel (TP) is a powerful tool for image over-segmentation. It is fast and can yield a lattice-like structure of superpixel regions with uniform size. This paper presents a method to learn eigen-images from the image to be segmented. Such eigen-images are used to generate the evolution speed in the TP framework. The task is formulated as a problem of pixel clustering. Specifically, for the pixels in each local window, a linear transformation is introduced to map their color vectors to be the cluster indicator vectors. The errors under all such linear transformations are estimated and summed together to obtain an objective function, from which a global optimum is finally obtained. In this process, the eigen-images are constructed. Based upon these eigen-images, multidimensional image gradient operator is defined to evaluate the gradient, which is supplied to the TP algorithm to obtain the final superpixel segmentations. The computational issues are discussed, and an image pyramid is introduced to speed up the computation. Comparative experiments illustrate the effectiveness of our method.

Multisource data registration based on NURBS description of contours

This paper presents a novel contour‐based approach for multisource image registration. The contours are parameterized with Non‐Uniform Rational B‐Splines (NURBS). The control points of parametric contours are used as contour descriptor for image registration due to their invariance under affine and perspective transformations. The distance of control points, and the curvature and orientation similarity of the corresponding segments induced by the control points are considered as the matching criteria, and mismatching of control points can be avoided effectively because of the local controllability of NURBS. Therefore, the method is able to deal with the case in which the corresponding contours are locally distorted. Additionally, the NURBS description of contours has the strong global property; the method is therefore robust to image noise. In order to improve robustness, we perform the extraction and labelling of contours interactively. The experiments on both single‐sensor and multisource data registration demonstrate the effectiveness and robustness of the presented method.

A semi-supervised framework for mapping data to the intrinsic manifold

This paper presents a novel scheme for manifold learning. Different from the previous work reducing data to Euclidean space which cannot handle the looped manifold well, we map the scattered data to its intrinsic parameter manifold by semisupervised learning. Given a set of partially labeled points, the map to a specified parameter manifold is computed by an iterative neighborhood average method called anchor points diffusion procedure (APD). We explore this idea on the most frequently used close formed manifolds, Stiefel manifolds whose special cases include hyper sphere and orthogonal group. The experiments show that APD can recover the underlying intrinsic parameters of points on scattered data manifold successfully.

Skew Estimation of Document Images Using Bagging

This paper proposes a general-purpose method for estimating the skew angles of document images. Rather than to derive a skew angle merely from text lines, the proposed method exploits various types of visual cues of image skew available in local image regions. The visual cues are extracted by Radon transform and then outliers of them are iteratively rejected through a floating cascade. A bagging (bootstrap aggregating) estimator is finally employed to combine the estimations on the local image blocks. Our experimental results show significant improvements against the state-of-the-art methods, in terms of execution speed and estimation accuracy, as well as the robustness to short and sparse text lines, multiple different skews and the presence of nontextual objects of various types and quantities.

Interactive Image Segmentation With Multiple Linear Reconstructions in Windows

This paper proposes an algorithm for interactive image segmentation. The task is formulated as a problem of graph-based transductive classification. Specifically, given an image window, the color of each pixel in it will be reconstructed linearly with those of the remaining pixels in this window. The optimal reconstruction weights will be kept unchanged to linearly reconstruct their class labels. The label reconstruction errors are estimated in each window. These errors are further collected together to develop a learning model. Then, the class information about the user specified foreground and background pixels are integrated into a regularization framework. Under this framework, a globally optimal labeling is finally obtained. The computational complexity is analyzed, and an approach for speeding up the algorithm is presented. Comparative experimental results illustrate the validity of our algorithm.

3-D Head Tracking via Invariant Keypoint Learning

Keypoint matching is a standard tool to solve the correspondence problem in vision applications. However, in 3-D face tracking, this approach is often deficient because the human face complexities, together with its rich viewpoint, nonrigid expression, and lighting variations in typical applications, can cause many variations impossible to handle by existing keypoint detectors and descriptors. In this paper, we propose a new approach to tailor keypoint matching to track the 3-D pose of the user head in a video stream. The core idea is to learn keypoints that are explicitly invariant to these challenging transformations. First, we select keypoints that are stable under randomly drawn small viewpoints, nonrigid deformations, and illumination changes. Then, we treat keypoint descriptor learning at different large angles as an incremental scheme to learn discriminative descriptors. At matching time, to reduce the ratio of outlier correspondences, we use second-order color information to prune keypoints unlikely to lie on the face. Moreover, we integrate optical flow correspondences in an adaptive way to remove motion jitter efficiently. Extensive experiments show that the proposed approach can lead to fast, robust, and accurate 3-D head tracking results even under very challenging scenarios.

Consistent Correspondence between Arbitrary Manifold Surfaces

We propose a novel framework for consistent correspondence between arbitrary manifold meshes. Different from most existing methods, our approach directly maps the connectivity of the source mesh onto the target mesh without needing to segment input meshes, thus effectively avoids dealing with unstable extreme conditions (e.g. complex boundaries or high genus). In this paper, firstly, a novel mean-value Laplacian fitting scheme is proposed, which aims at computing a shape-preserving (conformal) correspondence directly in 3D-to-3D space, efficiently avoiding local optimum caused by the nearest-point search, and achieving good results even with only a few marker points. Secondly, we introduce a vertex relocation and projection approach, which refines the initial fitting result in the way of local conformity. Each vertex of the initial result is gradually projected onto the target models surface to ensure a complete surface match. Furthermore, we provide a fast and effective approach to automatically detect critic points in the context of consistent correspondence. By fitting these critic points that capture the important features of the target mesh, the output compatible mesh matches the target meshs profiles quite well. Compared with previous approaches, our scheme is robust, fast, and convenient, thus suitable for common applications.