Jose San Pedro
Nuria Oliver
ABSTRACT
The increasing number of images available online has created a growing need for efficient ways to search for relevant content. Text-based query search is the most common approach to retrieve images from the Web. In this approach, the similarity between the input query and the metadata of images is used to find relevant information. However, as the amount of available images grows, the number of relevant images also increases, all of them sharing very similar metadata but differing in other visual characteristics. This paper studies the influence of visual aesthetic quality in search results as a complementary attribute to relevance. By considering aesthetics, a new ranking parameter is introduced aimed at improving the quality at the top ranks when large amounts of relevant results exist. Two strategies for aesthetic rating inference are proposed: one based on visual content, another based on the analysis of user comments to detect opinions about the quality of images. The results of a user study with $58$ participants show that the comment-based aesthetic predictor outperforms the visual content-based strategy, and reveals that aesthetic-aware rankings are preferred by users searching for photographs on the Web.
- R. Datta, D. Joshi, J. Li, and J. Z. Wang. Studying Aesthetics in Photographic Images Using a Computational Approach. In ECCV'06, volume 3953 of L.N. in Computer Science, pages 288--301. Springer. Google Scholar
Digital Library
- S. Hasler and S. Susstrunk. Measuring colorfulness in real images. volume 5007, pages 87--95, 2003.Google Scholar
- W. H. Hsu, L. S. Kennedy, and S. F. Chang. Video search reranking via information bottleneck principle. In ACM Multimedia '06, pages 35--44, NY, USA, 2006. Google ScholarDigital Library
- K. Q. Huang, Q. Wang, and Z. Y. Wu. Natural color image enhancement and evaluation algorithm based on human visual system. Comput. Vis. Image Underst., 103(1):52--63, 2006. Google ScholarDigital Library
- V. Jain and M. Varma. Learning to re-rank: query-dependent image re-ranking using click data. In Proc. ACM Conf. on World wide web, WWW '11, pages 277--286, NY, USA, 2011. ACM. Google ScholarDigital Library
- W. Jin, H. H. Ho, and R. K. Srihari. OpinionMiner: a novel machine learning system for web opinion mining and extraction. In Proc. ACM SIGKDD, KDD '09, pages 1195--1204, NY, USA, 2009. ACM. Google ScholarDigital Library
- Y. Jing and S. Baluja. Pagerank for product image search. In Proc. ACM Conf. on World Wide Web, WWW '08, pages 307--316, NY, USA, 2008. ACM. Google ScholarDigital Library
- Y. Ke, X. Tang, and F. Jing. The Design of High-Level Features for Photo Quality Assessment. Computer Vision and Pattern Recognition, 2006 IEEE Computer Society Conference on, 1:419--426, June 2006. Google ScholarDigital Library
- C. W. K. Leung, S. C. F. Chan, F. L. Chung, and G. Ngai. A probabilistic rating inference framework for mining user preferences from reviews. World Wide Web, 14(2):187--215, Mar. 2011. Google ScholarDigital Library
- Y. Luo and X. Tang. Photo and Video Quality Evaluation: Focusing on the Subject. In ECCV '08, pages 386--399, Berlin, Heidelberg, 2008. Springer-Verlag. Google ScholarDigital Library
- P. Obrador, X. Anguera, R. de Oliveira, and N. Oliver. The role of tags and image aesthetics in social image search. In WSM '09, pages 65--72, NY, USA, 2009. ACM. Google ScholarDigital Library
- P. Obrador, L. Schmidt-Hackenberg, and N. Oliver. The role of image composition in image aesthetics. In IEEE ICIP 2010, pages 3185--3188, 2010.Google ScholarCross Ref
- R. Orendovici and J. Z. Wang. Training data collection system for a learning-based photographic aesthetic quality inference engine. In ACM Multimedia'10, pages 1575--1578, NY, USA, 2010. Google ScholarDigital Library
- B. Pang and L. Lee. Seeing stars: exploiting class relationships for sentiment categorization with respect to rating scales. In Proceedings of the 43rd Annual Meeting on Association for Computational Linguistics, ACL '05, pages 115--124, Stroudsburg, PA, USA, 2005. Association for Computational Linguistics. Google ScholarDigital Library
- G. Peters. Aesthetic Primitives of Images for Visualization. In IEEE Int. Conf. Information Visualization, 2007, pages 316--325, July 2007. Google ScholarDigital Library
- J. San Pedro and S. Siersdorfer. Ranking and classifying attractiveness of photos in folksonomies. In Proc. ACM conf. on World wide web, WWW '09, pages 771--780, NY, USA, 2009. Google ScholarDigital Library
- N. Sawant, J. Li, and J. Z. Wang. Automatic image semantic interpretation using social action and tagging data. Multimedia Tools Appl., 51(1):213--246, 2011. Google ScholarDigital Library
- A. Smola and B. Schölkopf. A tutorial on support vector regression. Statistics and Computing, 14(3):199--222, Aug. 2004. Google ScholarDigital Library
- R. van Zwol, A. Rae, and L. G. Pueyo. Prediction of favourite photos using social, visual, and textual signals. In ACM Multimedia'10, pages 1015--1018, NY, USA, 2010. Google ScholarDigital Library
- M. Wang, B. Liu, and X. S. Hua. Accessible image search. In ACM Multimedia'09, pages 291--300, NY, USA, 2009. Google ScholarDigital Library
- L. Yang and A. Hanjalic. Supervised reranking for web image search. In ACM Multimedia'10, pages 183--192, NY, USA, 2010. Google ScholarDigital Library
- C. H. Yeh, Y. C. Ho, B. A. Barsky, and M. Ouhyoung. Personalized photograph ranking and selection system. In ACM Multimedia'10, pages 211--220, NY, USA, 2010. Google ScholarDigital Library
- J. Yu, Z.-J. Zha, M. Wang, and T.-S. Chua. Aspect ranking : Identifying important product aspects from online consumer reviews. Computational Linguistics, pages 1496--1505, 2011. Google ScholarDigital Library
- Z. J. Zha, L. Yang, T. Mei, M. Wang, Z. Wang, T. S. Chua, and X. S. Hua. Visual query suggestion: Towards capturing user intent in internet image search. ACM Trans. Multimedia Comput. Commun. Appl., 6, Aug. 2010. Google ScholarDigital Library
Index Terms
- Leveraging user comments for aesthetic aware image search reranking
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