{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,7,30]],"date-time":"2025-07-30T14:19:47Z","timestamp":1753885187563,"version":"3.41.2"},"reference-count":11,"publisher":"World Scientific Pub Co Pte Ltd","issue":"02","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Int. J. Comp. Intel. Appl."],"published-print":{"date-parts":[[2023,6]]},"abstract":"<jats:p> Group photo images are everywhere and vary greatly by the shooting scene. Compared with common images, the Image Aesthetic Quality Assessment (IAQI) of group photo pays more attention to the relevant characteristics of the main population. Still, the existing methods do not make further special research on group photos. Therefore, we propose a new concept of group photo styling based on analyzing group photos and photographic theory. Besides that, by comparing and analyzing many group photos, we classify the group photos into five categories. In this paper, the main factors of the head and pose are considered simultaneously, and the method of Group Photo Styling Classification (GPSC) can classify different group photos automatically. To verify the effectiveness of our method, we collected a Group Photo Styling Dataset (GPSD). The dataset contains 998 group photo images, and each image\u2019s group photo styling category is marked. The experimental results on GPSD show that the fusion of head features and pose features can classify different group photos well. The accuracy of GPSC reaches 93.9%, much higher than the previous classification model. <\/jats:p>","DOI":"10.1142\/s1469026823500104","type":"journal-article","created":{"date-parts":[[2023,5,4]],"date-time":"2023-05-04T07:39:10Z","timestamp":1683185950000},"source":"Crossref","is-referenced-by-count":0,"title":["Styling Classification of Group Photos Fusing Head and Pose Features"],"prefix":"10.1142","volume":"22","author":[{"given":"Kai","family":"Wang","sequence":"first","affiliation":[{"name":"School of Computer Science and Technology, Tiangong University, Tianjin, P.\u00a0R.\u00a0China"},{"name":"Tianjin Key Laboratory of Autonomous Intelligence Technology and Systems, Tianjin, P.\u00a0R.\u00a0China"}]},{"given":"Congwei","family":"Guo","sequence":"additional","affiliation":[{"name":"School of Computer Science and Technology, Tiangong University, Tianjin, P.\u00a0R.\u00a0China"}]},{"given":"Zhuang","family":"Zhao","sequence":"additional","affiliation":[{"name":"School of Computer Science and Technology, Tiangong University, Tianjin, P.\u00a0R.\u00a0China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2792-8728","authenticated-orcid":false,"given":"Yongzhen","family":"Ke","sequence":"additional","affiliation":[{"name":"Tianjin Key Laboratory of Autonomous Intelligence Technology and Systems, Tianjin, P.\u00a0R.\u00a0China"},{"name":"School of Computer Science and Technology, Tiangong University, Tianjin 300387, P.\u00a0R.\u00a0China"}]},{"given":"Shuai","family":"Yang","sequence":"additional","affiliation":[{"name":"Tianjin Key Laboratory of Autonomous Intelligence Technology and Systems, Tianjin, P.\u00a0R.\u00a0China"},{"name":"School of Computer Science and Technology, Tiangong University, Tianjin 300387, P.\u00a0R.\u00a0China"}]}],"member":"219","published-online":{"date-parts":[[2023,5,4]]},"reference":[{"issue":"8","key":"S1469026823500104BIB002","doi-asserted-by":"crossref","first-page":"3998","DOI":"10.1109\/TIP.2018.2831899","volume":"27","author":"Talebi H.","year":"2018","journal-title":"IEEE Trans. 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