Affective image retrieval via multi-graph learning

MM 2014 - Proceedings of the 2014 ACM Conference on Multimedia

Volumn , Issue , 2014, Pages 1025-1028

  Zhao, Sicheng ^a   Yao, Hongxun ^a   Yang, You ^b   Zhang, Yanhao ^a  

^a HARBIN INSTITUTE OF TECHNOLOGY   (China)

^b HUAZHONG UNIVERSITY OF SCIENCE AND TECHNOLOGY   (China)

Author keywords

Affective image retrieval; Image emotion; Multi graph learning

Indexed keywords

ARTS COMPUTING; CLASSIFICATION (OF INFORMATION); SEMANTICS;

AFFECTIVE IMAGES; FACIAL EXPRESSIONS; HIGH-LEVEL FEATURES; IMAGE EMOTION; LEARNING FRAMEWORKS; MULTI-GRAPH LEARNING; REGULARIZATION FRAMEWORK; RETRIEVAL PERFORMANCE;

IMAGE RETRIEVAL;

EID: 84913552813     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1145/2647868.2655035     Document Type: Conference Paper

References (21)

1. D. Borth et al. Large-scale visual sentiment ontology and detectors using adjective noun pairs. In MM, 2013.
2. E. S. Dan-Glauser and K. R. Scherer. The geneva affective picture database (gaped): a new 730-picture database focusing on valence and normative significance. Behavior research methods, 43(2):468-477, 2011.
3. Y. Gao et al. 3-d object retrieval and recognition with hypergraph analysis. IEEE TIP, 21(9):4290-4303, 2012.
4. A. Hanjalic. Extracting moods from pictures and sounds: Towards truly personalized tv. IEEE Signal Processing Magazine, 23(2):90-100, 2006.
5. K. Järvelin and J. Kekäläinen. Cumulated gain-based evaluation of ir techniques. ACM TOIS, 20(4):422-446, 2002.
6. J. Jia et al. Can we understand van gogh's mood? learning to infer affects from images in social networks. In MM, 2012.
7. P. Lang et al. International affective picture system (IAPS): Affective ratings of pictures and instruction manual. NIMH, Center for the Study of Emotion & Attention, 2005.
8. X. Lu et al. On shape and the computability of emotions. In MM, 2012.
9. P. Lucey et al. The extended cohn-kanade dataset (ck+): A complete dataset for action unit and emotion-specified expression. In CVPR Workshops, 2010.
10. J. Machajdik and A. Hanbury. Affective image classification using features inspired by psychology and art theory. In MM, 2010.
11. J. Mikels et al. Emotional category data on images from the international affective picture system. Behavior research methods, 37(4):626-630, 2005.
12. B. Pang and L. Lee. Opinion mining and sentiment analysis. Information Retrieval, 2(1-2):1-135, 2008.
13. G. Patterson and J. Hays. Sun attribute database: Discovering, annotating, and recognizing scene attributes. In CVPR, 2012.
14. P. Viola et al. Robust real-time face detection. IJCV, 57(2):137-154, 2004.
15. M. Wang et al. Unified video annotation via multigraph learning. IEEE TCSVT, 19(5):733-746, 2009.
16. W. Wang et al. A survey on emotional semantic image retrieval. In ICIP, 2008.
17. J. Xiao et al. Sun database: Large-scale scene recognition from abbey to zoo. In CVPR, 2010.
18. P. Yang et al. Exploring facial expressions with compositional features. In CVPR, 2010.
19. J. Yuan et al. Sentribute: image sentiment analysis from a mid-level perspective. In WISDOM, 2013.
20. S. Zhao et al. Video indexing and recommendation based on affective analysis of viewers. In ACM MM, 2011.
21. S. Zhao et al. Exploring principles-of-art features for image emotion recognition. In ACM MM, 2014.