Iterative Improvement of Human Pose Classification Using Guide Ontology

Kazuhiro TASHIRO  Takahiro KAWAMURA  Yuichi SEI  Hiroyuki NAKAGAWA  Yasuyuki TAHARA  Akihiko OHSUGA  

IEICE TRANSACTIONS on Information and Systems   Vol.E99-D    No.1    pp.236-247
Publication Date: 2016/01/01
Publicized: 2015/10/01
Online ISSN: 1745-1361
DOI: 10.1587/transinf.2015EDP7067
Type of Manuscript: PAPER
Category: Image Recognition, Computer Vision
ontology,  semantic web,  knowledge representation,  

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The objective of this paper is to recognize and classify the poses of idols in still images on the web. The poses found in Japanese idol photos are often complicated and their classification is highly challenging. Although advances in computer vision research have made huge contributions to image recognition, it is not enough to estimate human poses accurately. We thus propose a method that refines result of human pose estimation by Pose Guide Ontology (PGO) and a set of energy functions. PGO, which we introduce in this paper, contains useful background knowledge, such as semantic hierarchies and constraints related to the positional relationship between body parts. Energy functions compute the right positions of body parts based on knowledge of the human body. Through experiments, we also refine PGO iteratively for further improvement of classification accuracy. We demonstrate pose classification into 8 classes on a dataset containing 400 idol images on the web. Result of experiments shows the efficiency of PGO and the energy functions; the F-measure of classification is 15% higher than the non-refined results. In addition to this, we confirm the validity of the energy functions.