High-level feature extraction using SVM with walk-based graph kernel

  • Authors:

  • Jean-Philippe Vert;Tomoko Matsui;Shin'ichi Satoh;Yuji Uchiyama

  • Affiliations:

  • Centre for Computational Biology, Mines ParisTech, Fontainebleau, France;Institute of Statistical Mathematics, Tokyo, Japan;National Institute of Informatics, Tokyo, Japan;Picolab Co., Ltd, Tokyo, Japan

  • Venue:
  • ICASSP '09 Proceedings of the 2009 IEEE International Conference on Acoustics, Speech and Signal Processing
  • Year:
  • 2009

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Abstract

We investigate a method using support vector machines (SVMs) with walk-based graph kernels for high-level feature extraction from images. In this method, each image is first segmented into a finite set of homogeneous segments and then represented as a segmentation graph where each vertex is a segment and edges connect adjacent segments. Given a set of features associated with each segment, we then obtain a positive definite kernel between images by comparing walks in the respective segmentation graphs, and image classification is carried out with an SVM based on this kernel. In a benchmark experiment on the MediaMill challenge problem, the mean average precision increased from 0.216 (baseline) to 0.341 when our method was utilized.