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Integration of Top-down and Bottom-up Information for Image Labeling

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3 Author(s)
Toyoda, T. ; Tokyo Institute of Technology ; Tagami, K. ; Hasegawa, O.

This paper proposes a novel framework that integrates bottom-up information and top-down information for scene understanding. Bottom-up information is derived from local features of texture and color. Top-down information is generated from a holistic image context. The information is integrated effectively by extension of the Ising model, which is a simple model of ferromagnetism. Locally and globally consistent image recognition is achieved through an iterative process. The proposed method showed 91.8% accuracy in road-image labeling, which is superior to results obtained using only bottom-up information (81.9%) and the best accuracy obtained using the other method (90.7%).

Published in:

Computer Vision and Pattern Recognition, 2006 IEEE Computer Society Conference on  (Volume:1 )

Date of Conference:

17-22 June 2006