In this paper, we propose a novel learning method, called Jensen-Shannon Boosting (JSBoost) and demonstrate its application to object recognition. JSBoost incorporates Jensen-Shan...
In this a novel supervised learning method is proposed to map low-level visualfeatures to high-level semantic conceptsfor region-based image retrieval. The contributions of thispa...
Wei Jiang, Kap Luk Chan, Mingjing Li, HongJiang Zh...
Here we explore a discriminative learning method on underlying generative models for the purpose of discriminating between object categories. Visual recognition algorithms learn m...
In this paper we show that Ullman and Basri’s linear combination (LC) representation, which was originally proposed for alignment-based object recognition, can be used for outli...
We introduce a new method that characterizes typical local image features (e.g., SIFT [9], phase feature [3]) in terms of their distinctiveness, detectability, and robustness to i...
A novel framework called 2D Fisher Discriminant Analysis
(2D-FDA) is proposed to deal with the Small Sample
Size (SSS) problem in conventional One-Dimensional Linear
Discriminan...
Hui Kong, Lei Wang, Eam Khwang Teoh, Jian-Gang Wan...
We combine local texture features (PCA-SIFT), global features (shape context), and spatial features within a single multi-layer AdaBoost model of object class recognition. The fir...
Wei Zhang 0002, Bing Yu, Gregory J. Zelinsky, Dimi...
Nearest neighborhood consistency is an important concept in statistical pattern recognition, which underlies the well-known k-nearest neighbor method. In this paper, we combine th...
Functional Magnetic Resonance Imaging (fMRI) has enabled scientists to look into the active human brain. FMRI provides a sequence of 3D brain images with intensities representing ...
Lei Zhang 0002, Dimitris Samaras, Dardo Tomasi, No...
This paper presents a novel approach for estimating parameters for MRF-based stereo algorithms. This approach is based on a new formulation of stereo as a maximum a posterior (MAP...