Abstract: We present a novel approach for multi-view car detection using unsupervised sub-categorization instead of manual labeling. Cars have large variability of models and the view-point makes the appearance change dramatically. For object classes with a large intra-class variation like cars, a divide-and-conquer strategy may be applied. Instead of using manually predefined intra-class subcategorization, we examine several non-linear dimension reduction methods and group samples in the low-dimension embedding in an unsupervised way. The clustered samples have strong view-point similarities internally. A boosting-based cascade tree classifier is trained based on these subcategorizations. To demonstrate the capability of our multiview car detector, we create a more challenging test set with annotations. Compared to the UIUC side-view car data set, our test set contains a large range of car models, view points, and complex backgrounds. We compare our approach with previous methods and the result shows that ours outperforms the state-of-the-art methods. Bio: Cheng-Hao Kuo was born in Taipei, Taiwan. He received the BS degree in electrical engineering from National Taiwan University in 2002, and the MS degree in electrical and computer engineering from Carnegie Mellon University in 2005. He is currently a PhD student in the computer vision lab at University of Southern California, under the supervision of Professor Ram Nevatia. His research interests include computer vision and machine learning, especially for object detection and pedestrian tracking.