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Text detection in natural scene images is an important prerequisite for many content-based image analysis tasks. In this paper, we propose an accurate and robust method for detecting texts in natural scene images. A fast and effective pruning algorithm is designed to extract Maximally Stable Extremal Regions (MSERs) as character candidates using the… (More)

In this paper we study the problem of learning a low-rank (sparse) distance matrix. We propose a novel metric learning model which can simultaneously conduct dimension reduction and learn a distance matrix. The sparse representation involves a mixed-norm regularization which is non-convex. We then show that it can be equivalently formulated as a convex… (More)

Keywords: Sparse representation Implementations of L 0-norm Regularization term Support vector machine Kernel methods a b s t r a c t This paper provides a sparse learning algorithm for Support Vector Classification (SVC), called Sparse Support Vector Classification (SSVC), which leads to sparse solutions by automatically setting the irrelevant parameters… (More)

We construct a distribution-free Bayes optimal classifier called the Minimum Error Minimax Probability Machine (MEMPM) in a worst-case setting, i.e., under all possible choices of class-conditional densities with a given mean and covariance matrix. By assuming no specific distributions for the data, our model is thus distinguished from traditional Bayes… (More)

We consider the problem of the binary classification on im-balanced data, in which nearly all the instances are labelled as one class, while far fewer instances are labelled as the other class, usually the more important class. Traditional machine learning methods seeking an accurate performance over a full range of instances are not suitable to deal with… (More)

Sparse Metric Learning (SML), capable of learning both a good distance metric and low-dimension representations simultaneously, has received much attention recently. However , performance of existing sparse metric learning approaches is usually limited because they either made some relaxations or targeted the SML objective indirectly. In this paper, we… (More)

Most existing distance metric learning methods assume perfect side information that is usually given in pairwise or triplet constraints. Instead, in many real-world applications, the constraints are derived from side information, such as users' implicit feedbacks and citations among articles. As a result, these constraints are usually noisy and contain many… (More)

Zero-norm, defined as the number of non-zero elements in a vector, is an ideal quantity for feature selection. However , minimization of zero-norm is generally regarded as a combinatorially difficult optimization problem. In contrast to previous methods that usually optimize a surrogate of zero-norm, we propose a direct optimization method to achieve… (More)

A new large margin classifier, named Maxi-Min Margin Machine (M<sup>4</sup>) is proposed in this paper. This new classifier is constructed based on both a "local: and a "global" view of data, while the most popular large margin classifier, Support Vector Machine (SVM) and the recently-proposed important model, Minimax Probability Machine (MPM) consider data… (More)