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Dongqing Zhang, Shih-Fu Chang. Learning Random Attributed Relational Graph for Part-based Object Detection. ADVENT Technical Report #212-2005-6 Columbia University, May 2005.

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Part-based object detection methods have been shown intuitive and effective in detecting general object classes. However, their practical power is limited due to the need of part-level labels for supervised learning and the low learning speed. In this report, we present a new model called Random Attributed Relational Graph (RARG), by which we show that part matching and model learning can be achieved by combining variational learning methods with the part-based representations. We also discover an important mathematical property relating the object detection likelihood ratio and the partition functions of the Markov Random Field (MRF) in the model. Our approach demonstrates clear benefits over the state of the art in part-based object detection - 2 to 5 times faster in learning with almost the same detection accuracy. The improved learning efficiency allows us to extend the single RARG model to a mixture model for learning and detecting multi-view objects


Dongqing Zhang
Shih-Fu Chang

BibTex Reference

   Author = {Zhang, Dongqing and Chang, Shih-Fu},
   Title = {Learning Random Attributed Relational Graph       for Part-based Object Detection},
   Institution = {Columbia University},
   Month = {May},
   Year = {2005}

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