Intelligent Annotation and Retrieval for Very Large Scale Image Databases
James Wang, Penn State

The need for efficient content-based large scale image retrieval has increased tremendously in many application areas such as biomedicine, military, commerce, education, and Web image classification and searching. In this talk, we present our research in the area of intelligent image indexing, annotation and retrieval. We developed a wavelet-based approach for feature extraction and an integrated region matching (IRM) technique for matching region features. Our recent research has focused on developing ALIP (Automatic Linguistic Indexing of Pictures), a system to annotate images using automatically learned statistical models. Categorized images are used to train a dictionary of hundreds of concepts automatically based on statistical modeling. Images of any given concept category are regarded as instances of a stochastic process that characterizes the category. The work is a joint work with Dr. Jia Li of Statistics. More information about the projects is available at http://riemann.ist.psu.edu (or google the phrase 'image retrieval project') Short bio: James Z. Wang, holder of the PNC Technologies Career Development Professorship, is an assistant professor of the School of Information Sciences and Technology and by courtesy appointment in the Department of Computer Science and Engineering at The Pennsylvania State University. He received a Summa Cum Laude Bachelor's degree in Mathematics from University of Minnesota (1994), an M.S. in Mathematics and an M.S. in Computer Science, both from Stanford University (1997), and a Ph.D. degree from Stanford University's Biomedical Informatics and Database groups (2000). He is a recipient of an NSF Career award in support of his research program and PI of two other NSF-funded projects.