Abstract
Image aesthetic quality assessment is very useful in many multimedia applications. However, most existing researchers restrict quality assessment to a binary classification problem, which is to classify the aesthetic quality of images into “high” or “low” category. The strategy they applied is to learn the mapping from the aesthetic features to the absolute binary labels of images. The binary label description is restrictive and fails to capture the general relative relationship between images. We propose a pairwise-based ranking framework that takes image pairs as input to address this challenge. The main idea is to generate and select image pairs to utilize the relative ordering information between images rather than the absolute binary label information. We test our approach on two large scale and public datasets. The experimental results show our clear advantages over traditional binary classification-based approach.
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References
Bhattacharya, S., Sukthankar, R., Shah, M.: A framework for photo-quality assessment and enhancement based on visual aesthetics. In: Proceedings of the international conference on Multimedia, pp. 271–280. ACM (2010)
Datta, R., Joshi, D., Li, J., Wang, J.Z.: Studying aesthetics in photographic images using a computational approach. In: Leonardis, A., Bischof, H., Pinz, A. (eds.) ECCV 2006. LNCS, vol. 3953, pp. 288–301. Springer, Heidelberg (2006)
Deng, J., Dong, W., Socher, R., Li, L.J., Li, K., Fei-Fei, L.: Imagenet: a large-scale hierarchical image database. In: IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2009, pp. 248–255. IEEE (2009)
Dhar, S., Ordonez, V., Berg, T.L.: High level describable attributes for predicting aesthetics and interestingness. In: 2011 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 1657–1664. IEEE (2011)
Dong, Z., Shen, X., Li, H., Tian, X.: Photo quality assessment with DCNN that understands image well. In: He, X., Luo, S., Tao, D., Xu, C., Yang, J., Hasan, M.A. (eds.) MMM 2015, Part II. LNCS, vol. 8936, pp. 524–535. Springer, Heidelberg (2015)
DPChallenge: Dpchallenge. http://www.dpchallenge.com/
Joachims, T.: Training linear svms in linear time. In: Proceedings of the 12th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 217–226. ACM (2006)
Ke, Y., Tang, X., Jing, F.: The design of high-level features for photo quality assessment. In: 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, vol. 1, pp. 419–426. IEEE (2006)
Krizhevsky, A., Sutskever, I., Hinton, G.E.: Imagenet classification with deep convolutional neural networks. In: Advances in Neural Information Processing Systems, pp. 1097–1105 (2012)
Kumar, N., Berg, A.C., Belhumeur, P.N., Nayar, S.K.: Attribute and simile classifiers for face verification. In: 2009 IEEE 12th International Conference on Computer Vision, pp. 365–372. IEEE (2009)
Lu, X., Lin, Z., Jin, H., Yang, J., Wang, J.Z.: Rapid: rating pictorial aesthetics using deep learning. In: Proceedings of the ACM International Conference on Multimedia, pp. 457–466. ACM (2014)
Luo, W., Wang, X., Tang, X.: Content-based photo quality assessment. In: 2011 IEEE International Conference on Computer Vision (ICCV), pp. 2206–2213. IEEE (2011)
Luo, Y., Tang, X.: Photo and video quality evaluation: focusing on the subject. In: Forsyth, D., Torr, P., Zisserman, A. (eds.) ECCV 2008, Part III. LNCS, vol. 5304, pp. 386–399. Springer, Heidelberg (2008)
Marchesotti, L., Perronnin, F., Larlus, D., Csurka, G.: Assessing the aesthetic quality of photographs using generic image descriptors. In: 2011 IEEE International Conference on Computer Vision (ICCV), pp. 1784–1791. IEEE (2011)
Murray, N., Marchesotti, L., Perronnin, F.: Ava: a large-scale database for aesthetic visual analysis. In: 2012 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 2408–2415. IEEE (2012)
Parikh, D., Grauman, K.: Relative attributes. In: 2011 IEEE International Conference on Computer Vision (ICCV), pp. 503–510. IEEE (2011)
Su, H.H., Chen, T.W., Kao, C.C., Hsu, W.H., Chien, S.Y.: Scenic photo quality assessment with bag of aesthetics-preserving features. In: Proceedings of the 19th ACM International Conference on Multimedia, pp. 1213–1216. ACM (2011)
Tong, H., Li, M., Zhang, H.-J., He, J., Zhang, C.: Classification of digital photos taken by photographers or home users. In: Aizawa, K., Nakamura, Y., Satoh, S. (eds.) PCM 2004. LNCS, vol. 3331, pp. 198–205. Springer, Heidelberg (2004)
Wang, J., Song, Y., Leung, T., Rosenberg, C., Wang, J., Philbin, J., Chen, B., Wu, Y.: Learning fine-grained image similarity with deep ranking. In: 2014 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 1386–1393. IEEE (2014)
Acknowledgement
This work was supported by the 973 project under Contract 2015CB351803, by the NSFC under Contracts 61390514 and 61201413, by the Youth Innovation Promotion Association CAS No. CX2100060016, by the Fundamental Research Funds for the Central Universities No. WK2100060011 and No. WK2100100021, and by the Specialized Research Fund for the Doctoral Program of Higher Education No. WJ2100060003.
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Lv, H., Tian, X. (2016). Learning Relative Aesthetic Quality with a Pairwise Approach. In: Tian, Q., Sebe, N., Qi, GJ., Huet, B., Hong, R., Liu, X. (eds) MultiMedia Modeling. MMM 2016. Lecture Notes in Computer Science(), vol 9516. Springer, Cham. https://doi.org/10.1007/978-3-319-27671-7_41
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DOI: https://doi.org/10.1007/978-3-319-27671-7_41
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