Nandanwar, Lokesh and Shivakumara, Palaiahnakote and Krishnani, Divya and Ramachandra, Raghavendra and Lu, Tong and Pal, Umapada and Kankanhalli, Mohan (2021) A new foreground-background based method for behavior-oriented social media image classification. ACM Transactions on Multimedia Computing Communications and Applications, 17 (4). ISSN 1551-6857, DOI https://doi.org/10.1145/3458051.
Full text not available from this repository.Abstract
Due to various applications, research on personal traits using information on social media has become an important area. In this paper, a new method for the classification of behavior-oriented social images uploaded on various social media platforms is presented. The proposed method introduces a multimodality concept using skin of different parts of human body and background information, such as indoor and outdoor environments. For each image, the proposed method detects skin candidate components based on R, G, B color spaces and entropy features. The iterative mutual nearest neighbor approach is proposed to detect accurate skin candidate components, which result in foreground components. Next, the proposed method detects the remaining part (other than skin components) as background components based on structure tensor of R, G, B color spaces, and Maximally Stable Extremal Regions (MSER) concept in the wavelet domain. We then explore Hanman Transform for extracting context features from foreground and background components through clustering and fusion operation. These features are then fed to an SVM classifier for the classification of behavior-oriented images. Comprehensive experiments on 10-class datasets of Normal Behavior-Oriented Social media Image (NBSI) and Abnormal Behavior-Oriented Social media Image (ABSI) show that the proposed method is effective and outperforms the existing methods in terms of average classification rate. Also, the results on the benchmark dataset of five classes of personality traits and two classes of emotions of different facial expressions (FERPlus dataset) demonstrated the robustness of the proposed method over the existing methods.
| Item Type: | Article |
|---|---|
| Funders: | National Natural Science Foundation of China (NSFC)[61672273], Universiti Malaya[GPF014D-2019] |
| Uncontrolled Keywords: | Social media;Person traits;Person behavior;Hanman transform;Person identification;Behavior oriented;Social media image classification |
| Subjects: | Q Science > QA Mathematics > QA75 Electronic computers. Computer science |
| Divisions: | Faculty of Computer Science & Information Technology |
| Depositing User: | Ms Zaharah Ramly |
| Date Deposited: | 14 Oct 2022 05:15 |
| Last Modified: | 14 Oct 2022 05:15 |
| URI: | http://eprints.um.edu.my/id/eprint/35322 |
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