Saliency-enhanced image aesthetics class prediction

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Wong, L., Low, K.: Saliency-enhanced image aesthetics class prediction. In: ICIP 2009, pp. 997–1000. IEEE, Los Alamitos (2009).



We present a saliency-enhanced method for the classification of professional photos and snapshots. First, we extract the salient regions from an image by utilizing a visual saliency model. We assume that the salient regions contain the photo subject. Then, in addition to a set of discriminative global image features, we extract a set of salient features that characterize the subject and depict the subject-background relationship. Our high-level perceptual approach produces a promising 5-fold cross-validation (5-CV) classification accuracy of 78.8%, significantly higher than existing approaches that concentrate mainly on global features.

Extended Abstract


Used References

Coe, K. (1992), Art: The replicable unit - An inquiry into the possible origin of art as a social behavior, Journal of Social and Evolutionary Systems, 15(2), 217-234.

R. Datta, D. Joshi, J. Li, and J. Z. Wang (2006), Studying Aesthetics in Photographic Images Using a Computational Approach, Proc. of European Conference on Computer Vision, 288-301.

R. Datta, J. Li, and J. Z. Wang (2008), Algorithmic Inferencing of Aesthetics and Emotion in Natural Images: An Exposition, Proc. of IEEE International Conference on Image Processing, Special Session on Image Aesthetics, Mood and Emotion, 105-108.

Itti, L., Koch, C., Niebur, E. (1998), A Model of Saliency-Based Visual Attention for Rapid Scene Analysis, IEEE Transactions on Pattern Analysis and Machine Intelligence, 20(11), 1254-1259.

Lind, R. W. (1980), Attention and the Aesthetics Object, Journal of Aesthetics and Art Criticism, 39(2), 131-142.

Tong, H.,, Li, M., Zhang, H., He, J., and Zhang, C. (2002), Classification of Digital Photos Taken by Photographers or Home Users, Proc. of Pacific-Rim Conference on Multimedia. 367-376.

Wang, J.Z., Li, J., and Wiederhold, G., (2001), SIMPLIcity: Semantics-Sensitive Intergrated Matching for Picture Libraries, IEEE Transactions on Pattern Analysis and machine Intelligence, 23(9), 947-963. (Pubitemid 32981615)

Witten, I. H., and Frank, E., (2005), Data Mining: Practical machine learning tools and techniques, 2nd Edition, Morgan Kaufmann, San Francisco, 2005.

Yan Ke, Xiaoou Tang, Feng Jing (2006), The Design of High-Level Features for Photo Quality Assessment, Proc. of Computer Vision and Pattern Recognition, pp. 419-426.

Yang, A., Wright, J., and Yi, M. (2008), Unsupervised segmentation of natural images via lossy image compression, Computer Vision and Image Understanding, 110(2), 212-225.


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