caa a22 4500 606209948 CHVBK 20210128101031.0 cr unu---uuuuu 210128e20151201xx s 000 0 eng 10.1007/s11220-014-0102-z doi (NATIONALLICENCE)springer-10.1007/s11220-014-0102-z Bai Shuang School of Electronic and Information Engineering, Beijing Jiaotong University, Beijing, China aut Human-Centric Image Categorization Based on Poselets [Elektronische Daten] [Shuang Bai] In daily life, one kind of images are common, in which many people are present and performing certain activities. We call these images human-centric images. As the number of such images gets increasingly larger, to organize and access them efficiently becomes urgent. Since the categories of human-centric images are determined by human activities in the images, in this paper, we propose to classify human-centric images by analyzing poses of all humans in them. Specifically, first, we introduce the notion of poselets, which represent parts of poses of humans and a method to detect human based on the poselets. Given a human-centric image, to determine its category, we use the poselets and the human detection method to detect all possible poselet activations in it and create a statistical representation of the poses of humans in the image. Additionally, we also investigated the influence of contextual information on the categorization of human-centric images. Finally, for evaluating the human-centric image categorization method, five categories of human-centric images are collected from the internet and used for experiments. Experiment results show that the poselet distribution representations are more suitable for representing human-centric images than the popular bag of visual words method. Springer Science+Business Media New York, 2014 Human-centric images nationallicence Human activity nationallicence Categorization nationallicence Poselet nationallicence Sensing and Imaging Springer US; http://www.springer-ny.com 16/1(2015-12-01), 1-19 1557-2064 16:1<1 2015 16 11220 https://doi.org/10.1007/s11220-014-0102-z text/html Onlinezugriff via DOI BK010053 XK010053 XK010000 Metadata rights reserved Springer special CC-BY-NC licence nationallicence 1 research-article jats NATIONALLICENCE NATIONALLICENCE NL-springer NATIONALLICENCE 856 40 https://doi.org/10.1007/s11220-014-0102-z text/html Onlinezugriff via DOI NATIONALLICENCE 100 1- Bai Shuang School of Electronic and Information Engineering, Beijing Jiaotong University, Beijing, China aut NATIONALLICENCE 773 0- Sensing and Imaging Springer US; http://www.springer-ny.com 16/1(2015-12-01), 1-19 1557-2064 16:1<1 2015 16 11220