This material is presented to ensure timely dissemination of scholarly and technical work. Copyright and all rights therein are retained by authors or by other copyright holders. All persons copying this information are expected to adhere to the terms and constraints invoked by each author's copyright. In most cases, these works may not be reposted without the explicit permission of the copyright holder.

Search for Publication

Year(s) from:  to 
Keywords (separated by spaces):

Global and Efficient Self-Similarity for Object Classification and Detection

T. Deselaers and V. Ferrari
CVPR 2010
June 2010


Self-similarity is an attractive image property which has recently found its way into object recognition in the form of local self-similarity descriptors. In this paper we explore global self-similarity (GSS) and its advantages over local self-similarity (LSS). We make three contributions: (a) we propose computationally efficient algorithms to extract GSS descriptors for classification. These capture the spatial arrangements of self-similarities withing the entire image; (b) we show how to use these descriptors efficiently for detection in a sliding-window framework and in a branch-and-bound framework; (c) we experimentally demonstrate on \Pascal VOC 2007 and on \ETHZ Classes that GSS outperforms LSS for both classification and detection, and that GSS descriptors are complementary to conventional descriptors such as gradients or color

Download in pdf format
  author = {T. Deselaers and V. Ferrari},
  title = {Global and Efficient Self-Similarity for Object Classification and Detection},
  booktitle = {CVPR 2010},
  year = {2010},
  month = {June},
  keywords = {}