著者

Yasuto Arakaki Hayaru Shouno Kazuyuki Takahashi Takashi Morie

出版者

Information and Media Technologies Editorial Board

雑誌

Information and Media Technologies (ISSN:18810896)

巻号頁・発行日

vol.7, no.4, pp.1480-1488, 2012 (Released:2012-12-15)

参考文献数

15

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For the detection of generic objects in the field of image processing, histograms of orientation gradients (HOG) is discussed for these years. The performance of the classification system using HOG shows a good result. However, the performance of using HOG descriptor would be influenced by the detecting object size. In order to overcome this problem, we introduce a kind of hierarchy inspired from the convolution-net, which is a model of our visual processing system in the brain. The hierarchical HOG (H-HOG) integrates several scales of HOG descriptors in its architecture, and represents the input image as the combinatorial of more complex features rather than that of the orientation gradients. We investigate the H-HOG performance and compare with the conventional HOG. In the result, we obtain the better performance rather than the conventional HOG. Especially the size of representation dimension is much smaller than the conventional HOG without reducing the detecting performance.