This paper presents the results of the University at Buffalo in the 2006 ImageCLEFmed task. Our approach for this task combines Content Based Image Retrieval (CBIR) and text retrie...
In this paper, we propose a new approach to automatically clustering e-commerce search engines (ESEs) on the Web such that ESEs in the same cluster sell similar products. This all...
In this demo, we present IGroup, a Web image search engine that organizes the search results into semantic clusters. Different from all existing Web image search results clusterin...
The performance of many supervised and unsupervised learning algorithms is very sensitive to the choice of an appropriate distance metric. Previous work in metric learning and ada...
In this paper we present a novel system for content-based retrieval and classification of cultural relic images. First, the images are normalized to achieve rotation, translation a...