#### Filter Results:

#### Publication Year

2002

2016

#### Publication Type

#### Co-author

#### Publication Venue

#### Data Set Used

#### Key Phrases

Learn More

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)

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)

Recently, support vector machines (SVMs) have been engaged on relevance feedback tasks in content-based image retrieval. Typical approaches by SVMs treat the relevance feedback as a strict binary classification problem. However, these approaches do not consider an important issue of relevance feedback, i.e. the unbalanced dataset problem, in which the… (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 consider the problem of the binary classification on imbalanced 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)

In this paper, we propose a novel large margin classifier, called the maxi-min margin machine M(4). This model learns the decision boundary both locally and globally. In comparison, other large margin classifiers construct separating hyperplanes only either locally or globally. For example, a state-of-the-art large margin classifier, the support vector… (More)

There has been significant recent interest in sparse metric learning (SML) in which we simultaneously learn both a good distance metric and a low-dimensional representation. Unfortunately, the performance of existing sparse metric learning approaches is usually limited because the authors assumed certain problem relaxations or they target the SML objective… (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)

— Discriminative classifiers such as Support Vector Machines directly learn a discriminant function or a posterior probability model to perform classification. On the other hand, generative classifiers often learn a joint probability model and then use Bayes rules to construct a posterior classifier from this model. In general, generative classifiers are… (More)