ABSTRACT
Automatic image emotion analysis has emerged as a hot topic due to its potential application on high-level image understanding. Considering the fact that the emotion evoked by an image is not only from its global appearance but also interplays among local regions, we propose a novel affective image classification system based on bilayer sparse representation (BSR). The BSR model contains two layers: The global sparse representation (GSR) is to define global similarities between a test image and all the training images; and the local sparse representation (LSR) is to define similarities of local regions' appearances and their co-occurrence between a test image and all the training images. The experiments on real data sets demonstrate that our system is effective on image emotion recognition.
Supplemental Material
- C. Colombo, A. D. Bimbo, and P. Pala. Semantics in visual information retrieval. IEEE Multimedia, 6(3):38--53, 1999. Google ScholarDigital Library
- Y. Deng and B. S. Manjunath. Unsupervised segmentation of color-texture regions in images and video. IEEE TPAMI, 23(8):800--810, 2001. Google ScholarDigital Library
- B. Li, W. Xiong, and W. Hu. Web horror image recognition based on context-aware multi-instance learning. In Proc. of ICDM, pages 1158--1163, 2011. Google ScholarDigital Library
- B. Li, W. Xiong, W. Hu, and X. Ding. Context-aware affective images classification based on bilayer sparse representation. In Proc. of ACM MM, 2012. Google ScholarDigital Library
- S. Y. Liu, D. Xu, and S. H. Feng. Emotion categorization using affective-plsa model. Optical Engineering, 49(12):127201, 2010.Google ScholarCross Ref
- J. Machajdik and A. Hanbury. Affective image classification using features inspired by psychology and art theory. In Proc. of ACMMM, pages 83--92, 2010. Google ScholarDigital Library
- L. Ou, M. Luo, A. Woodcock, and A. Wright. A study of colour emotion and colour preference. part 1: Colour emotions for single colours. Color Res. & App., 29(3):232--240, 2004.Google ScholarCross Ref
- W. Wang and Q. He. A survey on emotional semantic image retrieval. In Proc. of ICIP, pages 117--120, 2008.Google ScholarCross Ref
- V. Yanulevskaya, J. C. van Gemert, and K. Roth. Emotional valence categorization using holistic image features. In Proc. of ICIP, pages 101--104, 2008.Google ScholarCross Ref
- X. Yuan and S. Yan. Visual classification with multi-task joint sparse representation. In Proc. of CVPR, pages 3493--3500, 2010.Google ScholarCross Ref
Index Terms
- Scaring or pleasing: exploit emotional impact of an image
Recommendations
Context-aware affective images classification based on bilayer sparse representation
MM '12: Proceedings of the 20th ACM international conference on MultimediaIn image understanding, the automatic recognition of emotion in an image is becoming important from an applicative viewpoint. Considering the fact that the emotion evoked by an image is not only from its global appearance but also interplays among local ...
Generating affective maps for images
Affective image analysis, which estimates humans' emotion reflection on images, has attracted increasing attention. Most of the existing methods focus on developing efficient visual features according to theoretical and empirical concepts, and extract ...
Exploring Principles-of-Art Features For Image Emotion Recognition
MM '14: Proceedings of the 22nd ACM international conference on MultimediaEmotions can be evoked in humans by images. Most previous works on image emotion analysis mainly used the elements-of-art-based low-level visual features. However, these features are vulnerable and not invariant to the different arrangements of ...
Comments