文章基本信息

标题：A Hierarchical Extension of the HOG Model Implemented in the Convolution-net for Human Detection
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作者：Yasuto Arakaki ; Hayaru Shouno ; Kazuyuki Takahashi 等
期刊名称：Information and Media Technologies
电子版ISSN：1881-0896
出版年度：2012
卷号：7
期号：4
页码：1480-1488
DOI：10.11185/imt.7.1480
出版社：Information and Media Technologies Editorial Board
摘要：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.
关键词：HOG model;convolution-net model;human detection;feature extraction