Learning beautiful (and ugly) attributes

BMVC 2013 - Electronic Proceedings of the British Machine Vision Conference 2013

Volumn , Issue , 2013, Pages

  Marchesotti, Luca ^a   Perronnin, Florent ^a  

^a XEROX RESEARCH CENTRE EUROPE   (France)

Author keywords

[No Author keywords available]

Indexed keywords

COMPUTER VISION;

AESTHETIC QUALITIES; INTERPRETABILITY; MID-LEVEL FEATURES; USER RATING; VISUAL APPEARANCE; VISUAL ATTRIBUTES;

IMAGE CLASSIFICATION;

EID: 84898448129     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.5244/C.27.7     Document Type: Conference Paper

References (36)

1. T. Berg, A. Berg, and J. Shih. Automatic attribute discovery and characterization from noisy web data. ECCV, 2010.
2. L. Bottou and O. Bousquet. The tradeoffs of large scale learning. In NIPS, 2007.
3. S. Clinchant, G. Csurka, F. Perronnin, and J.-M. Renders. Xrce participation to ImageEval. In ImageEval Workshop at CVIR, 2007.
4. G. Csurka, C. Dance, L. Fan, J. Willamowski, and C. Bray. Visual categorization with bags of keypoints. InWorkshop on statistical learning in computer vision, ECCV, 2004.
5. R. Datta, D. Joshi, J. Li, and J.Z. Wang. Studying aesthetics in photographic images using a computational approach. In ECCV, 2006.
6. R. Datta, J. Li, and J.Z. Wang. Algorithmic inferencing of aesthetics and emotion in natural images: An exposition. In ICIP, 2008.
7. S. Dhar, V. Ordonez, and T.L. Berg. High-level describable attributes for predicting aesthetics and interestingness. In CVPR, 2011.
8. K. Duan, D. Parikh, D. Crandall, and K. Grauman. Discovering localized attributes for fine-grained recognition. In CVPR, 2012.
9. A. Farhadi, I. Endres, D. Hoiem, and D. Forsyth. Describing objects by their attributes. In CVPR, 2009.
10. V. Ferrari and A. Zisserman. Learning visual attributes. NIPS, 2007.
11. T. Hofmann. Unsupervised learning by probabilistic latent semantic analysis. Machine Learning, 2001.
12. H. Jégou, M. Douze, and C. Schmid. Product quantization for nearest neighbor search. IEEE TPAMI, 2011.
13. Y. Ke, X. Tang, and F. Jing. The design of high-level features for photo quality assessment. In CVPR, 2006.
14. Kodak. How to take good pictures : A photo guide. Random House Inc, 1982.
15. C.H. Lampert, H. Nickisch, and S. Harmeling. Learning to detect unseen object classes by between-class attribute transfer. In CVPR, 2009.
16. D.G. Lowe. Object recognition from local scale-invariant features. In ICCV, 1999.
17. Y. Luo and X. Tang. Photo and video quality evaluation: Focusing on the subject. In ECCV, 2008.
18. L. Marchesotti, F. Perronnin, D. Larlus, and G. Csurka. Assessing the aesthetic quality of photographs using generic image descriptors. In ICCV, 2011.
19. N. Murray, L. Marchesotti, and F. Perronnin. Learning to rank images using semantic and aesthetic labels. In BMVC, 2012.
20. N. Murray, L. Marchesotti, and F. Perronnin. Ava: A large-scale database for aesthetic visual analysis. In CVPR, 2012.
21. A. Y. Ng, M. I. Jordan, Y. Weiss, et al. On spectral clustering: Analysis and an algorithm. NIPS, 2002.
22. A. Oliva and A. Torralba. Modeling the shape of the scene: A holistic representation of the spatial envelope. IJCV, 2001.
23. R. Orendovici and J.Z. Wang. Training data collection system for a learning-based photographic aesthetic quality inference engine. In ACM-MM, 2010.
24. B. Pang, L. Lee, and S. Vaithyanathan. Thumbs up?: Sentiment classification using machine learning techniques. In Proceedings of the ACL-02 conference on Empirical methods in natural language processing, 2012.
25. D. Parikh and K. Grauman. Interactively building a discriminative vocabulary of nameable attributes. In CVPR, 2011.
26. D. Parikh and K. Grauman. Relative attributes. In ICCV, 2011.
27. F. Perronnin and C. Dance. Fisher kernels on visual vocabularies for image categorization. In CVPR, 2007.
28. F. Perronnin, J. Sánchez, and T. Mensink. Improving the fisher kernel for large-scale image classification. In ECCV, 2010.
29. J. Li R. Datta and J. Z. Wang. Learning the consensus on visual quality for nextgeneration image management. In ACM-MM, 2007.
30. E. Riloff, S. Patwardhan, J. Wiebe, et al. Feature subsumption for opinion analysis. In Proceedings of the 2006 Conference on Empirical Methods in Natural Language Processing, 2006.
31. M. Rohrbach, M. Stark, G. Szarvas, I. Gurevych, and B. Schiele. What helps where- And why? semantic relatedness for knowledge transfer. In CVPR, 2010.
32. J. San Pedro, T. Yeh, and N. Oliver. Leveraging user comments for aesthetic aware image search reranking. In WWW, 2012.
33. R. Tibshirani. Regression shrinkage and selection via the lasso. Journal of the Royal Statistical Society. Series B (Methodological), 1996.
34. J.Wang, K. Markert, and M. Everingham. Learning models for object recognition from natural language descriptions. In BMVC, 2009.
35. K. Yanai and K. Barnard. Image region entropy: A measure of visualness of web images associated with one concept. In ACM-MM, 2005.
36. H. Zou and T. Hastie. Regularization and variable selection via the elastic net. Journal of the Royal Statistical Society, 2005.