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- Xu-Cheng Yin, Xuwang Yin, Kaizhu Huang, Hongwei Hao
- IEEE Transactions on Pattern Analysis and Machine…
- 2014

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)

- Yiming Ying, Kaizhu Huang, Colin Campbell
- NIPS
- 2009

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)

- Kaizhu Huang, Haiqin Yang, Irwin King, Michael R. Lyu, Lai-Wan Chan
- Journal of Machine Learning Research
- 2004

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)

- Chu-Hong Hoi, Chi-Hang Chan, Kaizhu Huang, M.R. Lyu, I. King
- 2004 IEEE International Joint Conference on…
- 2004

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)

- Kaizhu Huang, Danian Zheng, Jun Sun, Yoshinobu Hotta, Katsuhito Fujimoto, Satoshi Naoi
- Pattern Recognition Letters
- 2010

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)

- Kaizhu Huang, Haiqin Yang, Irwin King, Michael R. Lyu
- Proceedings of the 2004 IEEE Computer Society…
- 2004

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)

- Kaizhu Huang, Haiqin Yang, Irwin King, Michael R. Lyu
- IEEE Trans. Neural Networks
- 2008

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)

- Kaizhu Huang, Yiming Ying, Colin Campbell
- 2009 Ninth IEEE International Conference on Data…
- 2009

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)

- Kaizhu Huang, Haiqin Yang, Irwin King, Michael R. Lyu
- ICML
- 2004

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)