Aesthetics-Guided Graph Clustering with Absent Modalities Imputation

Zhang, Luming, Yao, Yiyang, Liu, Zhenguang and Shao, Ling (2019) Aesthetics-Guided Graph Clustering with Absent Modalities Imputation. IEEE Transactions on Image Processing, 28 (7). pp. 3462-3476. ISSN 1057-7149

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Abstract

Accurately clustering Internet-scale Internet users into multiple communities according to their aesthetic styles is a useful technique in image modeling and data mining. In this work, we present a novel partially-supervised model which seeks a sparse representation to capture photo aesthetics1. It optimally fuzes multi-channel features, i.e., human gaze behavior, quality scores, and semantic tags, each of which could be absent. Afterward, by leveraging the KL-divergence to distinguish the aesthetic distributions between photo sets, a large-scale graph is constructed to describe the aesthetic correlations between users. Finally, a dense subgraph mining algorithm which intrinsically supports outliers (i.e., unique users not belong to any community) is adopted to detect aesthetic communities. Comprehensive experimental results on a million-scale image set crawled from Flickr have demonstrated the superiority of our method. As a byproduct, the discovered aesthetic communities can enhance photo retargeting and video summarization substantially.

Item Type: Article
Uncontrolled Keywords: aesthetic community,clustering algorithms,computational modeling,feature extraction,flickr,gaze behavior,graph mining,machine learning,multimodal,partially-supervised,semantics,training,visualization,software,computer graphics and computer-aided design ,/dk/atira/pure/subjectarea/asjc/1700/1712
Faculty \ School: Faculty of Science > School of Computing Sciences
Related URLs:
Depositing User: LivePure Connector
Date Deposited: 01 Mar 2019 15:30
Last Modified: 22 Apr 2020 07:33
URI: https://ueaeprints.uea.ac.uk/id/eprint/70075
DOI: 10.1109/TIP.2019.2897940

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