MITSUBISHI ELECTRIC RESEARCH LABORATORIES http://www.merl.com ICA-based Probabilistic Local Appearance Models Xiang Zhou, Baback Moghaddam, Thomas Huang TR2001-29 December 2001 Abstract This paper proposes a novel image modeling scheme for object detection and localization. Object appearance is modeled by the joint distribution of k-tuple salient point feature vectors which are factorized component-wise after an independent component analysis (ICA). Also, we propose a distance-sensitive histograming technique for capturing spatial dependencies. The advantages over existing techniques include the ability to model non-rigid objects (at the expense of model- ing accuracy) and the flexibility in modeling spatial relationships. Experiments show that ICA does improve modeling accuracy and detection performance. Experiments in object detection in cluttered scenes have demonstrated promising results. International Conferent on Image Processing (ICIP ´ 01), Thessaloniki, Greece, October 8-10th, 2001 This work may not be copied or reproduced in whole or in part for any commercial purpose. Permission to copy in whole or in part without payment of fee is granted for nonprofit educational and research purposes provided that all such whole or partial copies include the following: a notice that such copying is by permission of Mitsubishi Electric Research Laboratories, Inc.; an acknowledgment of the authors and individual contributions to the work; and all applicable portions of the copyright notice. Copying, reproduction, or republishing for any other purpose shall require a license with payment of fee to Mitsubishi Electric Research Laboratories, Inc. All rights reserved. Copyright c Mitsubishi Electric Research Laboratories, Inc., 2001 201 Broadway, Cambridge, Massachusetts 02139