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.
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Kang, Y., Yamaguchi, K., Naito, T., Ninomiya, Y. (2011). Road Image Segmentation and Recognition Using Hierarchical Bag-of-Textons Method. In: Ho, YS. (eds) Advances in Image and Video Technology. PSIVT 2011. Lecture Notes in Computer Science, vol 7087. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-25367-6_22
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DOI: https://doi.org/10.1007/978-3-642-25367-6_22
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