Clustering data in high dimensions is believed to be a hard problem in general. A number of efficient clustering algorithms developed in recent years address this problem by proje...
Kamalika Chaudhuri, Sham M. Kakade, Karen Livescu,...
In this paper, we propose a probabilistic model for web image mining, which is based on concept-sensitive salient regions without human intervene. Our goal is to achieve a middle-...
Graph-based methods for semi-supervised learning have recently been shown to be promising for combining labeled and unlabeled data in classification problems. However, inference f...
In this paper we use a variational Bayesian framework for color image segmentation. Each image is represented in the L*u*v color coordinate system before being segmented by the va...
Abstract - The proposed algorithm in this work provides superresolution for color images by using a learning based technique that utilizes both generative and discriminant approach...