Road Image Segmentation and Recognition Using Hierarchical Bag-of-Textons Method

  • Yousun Kang
  • Koichiro Yamaguchi
  • Takashi Naito
  • Yoshiki Ninomiya
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7087)

Abstract

While the bag-of-words models are popular and powerful method for generic object recognition, they discard the context information for spatial layout. This paper presents a novel method for road image segmentation and recognition using a hierarchical bag-of-textons method. The histograms of extracted textons are concatenated to regions of interest with multi-scale regular grid windows. This method can learn automatically spatial layout and relative positions between objects in a road image. Experimental results show that the proposed hierarchical bag-of-textons method can effectively classify not only the texture-based objects, e.g. road, sky, sidewalk, building, but also shape-based objects, e.g. car, lane, of a road image comparing the conventional bag-of-textons methods for object recognition. In the future, the proposed system can combine with a road scene understanding system for vehicle environment perception.

Keywords

road image segmentation hierarchical bag-of-textons multi-scale 

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Copyright information

© Springer-Verlag Berlin Heidelberg 2011

Authors and Affiliations

  • Yousun Kang
    • 1
  • Koichiro Yamaguchi
    • 2
  • Takashi Naito
    • 2
  • Yoshiki Ninomiya
    • 2
  1. 1.Tokyo Polytechnic UniversityJapan
  2. 2.Toyota Central R&D Labs., Inc.Japan

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