Finding Objects in Large Collections of Images

Jitendra Malik, Computer Science Division, UC Berkeley

Seminar on People, Computers, and Design
Stanford University October 3, 1997


Retrieving images from very large collections using image content as a key is becoming an important problem. The UC Berkeley digital library group has adopted a particular perspective on the problem--namely, that users are primarily interested in scenes containing particular objects or configurations of objects. To make this possible, we believe the key aspects are (a) grouping image data into regions corresponding to objects, or parts of objects (b) recognizing particular configurations of regions as objects based on color, texture, shape or spatial arrangment (c) use learning techniques to assist in the acquisition of common chractersitics of visual categories.

We have defined a blob world representation which provides a transition from raw pixel data to a small set of localized coherent regions in color and texture space. Users can construct queries using the blob-world represntation; automatic machine learning techniques can use it to acquire visual categories such as tigers, eagles and airplanes based on a set of examples. We have also demonstrated the use of spatial arrangements of regions (a ``body plan'') to learn and recognize instances of humans and horses from a very large collection of images.


Jitendra Malik was born in Mathura, India in 1960. He received the B.Tech degree in Electrical Engineering from Indian Institute of Technology, Kanpur in 1980 and the PhD degree in Computer Science from Stanford University in 1986. In January 1986, he joined the faculty of the Computer Science Division, Department of EECS, University of California at Berkeley, where he is currently Professor and Vice-Chair of Graduate Matters. He is a member of the Cognitive Science and Vision Science groups at UC Berkeley. His research interests are in computer vision and computational modeling of human vision. His previous work has spanned a range of topics in early and intermediate vision including image segmentation, texture, stereopsis, motion analysis, and line drawing interpretation. Currently, he is also interested in the application of computer vision to intelligent vehicle highway systems. He received the gold medal for the best graduating student in Electrical Engineering from IIT Kanpur in 1980, a Presidential Young Investigator Award in 1989, and the Rosenbaum fellowship for the Computer Vision Programme at the Newton Institute of Mathematical Sciences, University of Cambridge in 1993.


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