Cat Head Detection - How to Effectively Exploit Shape and Texture Features

  • Weiwei Zhang
  • Jian Sun
  • Xiaoou Tang
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5305)


In this paper, we focus on the problem of detecting the head of cat-like animals, adopting cat as a test case. We show that the performance depends crucially on how to effectively utilize the shape and texture features jointly. Specifically, we propose a two step approach for the cat head detection. In the first step, we train two individual detectors on two training sets. One training set is normalized to emphasize the shape features and the other is normalized to underscore the texture features. In the second step, we train a joint shape and texture fusion classifier to make the final decision. We demonstrate that a significant improvement can be obtained by our two step approach. In addition, we also propose a set of novel features based on oriented gradients, which outperforms existing leading features, e. g., Haar, HoG, and EoH. We evaluate our approach on a well labeled cat head data set with 10,000 images and PASCAL 2007 cat data.


Texture Feature Object Detection Face Detection Human Detection Texture Detector 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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Copyright information

© Springer-Verlag Berlin Heidelberg 2008

Authors and Affiliations

  • Weiwei Zhang
    • 1
  • Jian Sun
    • 1
  • Xiaoou Tang
    • 2
  1. 1.Microsoft Research AsiaBeijingChina
  2. 2.Dept. of Information EngineeringThe Chinese University of Hong KongHong Kong

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