Summary

(to be updated soon)

We are investigating innovative techniques using integrated multimedia features for automatic image classification. This is a collaborative project with the Natural Language Processing group at Columbia. On the image side, we are developing a new approach, called OF*IIF (Object Frequency * Inverse Image Frequency), to automatically extract the discriminative objects and their distribution from single images or classes of images. The OF*IIF feature vector has been proved to be effective compared to other state-of-the-art image classifiers. It achieves significant performance gain when combined with the popular text-based approach, TF*IIF.

We are currently developing tan integration framework using Bayesian Networks for combining text-based and visual-based feature vectors for classifying images to different categories such as indoor/outdoor, people, handshake, etc. The demo shows automatic classification using combined text-visual features.

People

Demo

http://www.ctr.columbia.edu/~syp/cgi-bin/inLumine.html

Publication

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Last updated: June 12, 2002.