Road image segmentation and recognition using hierarchical bag-of-textons method

Authors:
Yousun Kang;Koichiro Yamaguchi;Takashi Naito;Yoshiki Ninomiya
Affiliations:
Tokyo Polytechnic University, Japan;Toyota Central R&D Labs., Inc., Japan;Toyota Central R&D Labs., Inc., Japan;Toyota Central R&D Labs., Inc., Japan
Venue:
PSIVT'11 Proceedings of the 5th Pacific Rim conference on Advances in Image and Video Technology - Volume Part I
Year:
2011

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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.