Learning the relative importance of objects from tagged images for retrieval and cross-modal search

International Journal of Computer Vision

Volumn 100, Issue 2, 2012, Pages 134-153

  Hwang, Sung Ju ^a   Grauman, Kristen ^a  

^a University of Texas at Austin   (United States)

Author keywords

Auto annotation; Cross modal retrieval; Image retrieval; Image search; Image tags; Kernelized canonical correlation analysis; Multi modal retrieval; Object recognition

Indexed keywords

AUTO-ANNOTATION; CANONICAL CORRELATION ANALYSIS; CROSS-MODAL; IMAGE SEARCH; IMAGE TAGS; MULTI-MODAL;

DATA PROCESSING; OBJECT RECOGNITION;

IMAGE RETRIEVAL;

EID: 84867097097     PISSN: 09205691     EISSN: 15731405     Source Type: Journal    
DOI: 10.1007/s11263-011-0494-3     Document Type: Article

References (47)

1. von Ahn, L., & Dabbish, L. (2004). Labeling images with a computer game. In CHI.
2. Akaho, S. (2001). A kernel method for canonical correlation analysis. In International meeting of Psychometric Society.
3. Baeza-Yates, R., & Ribeiro-Neto, B. (1999). Modern information retrieval. Reading: Addison Wesley.
4. Barnard, K., Duygulu, P., de Freitas, N., Forsyth, D., Blei, D., & Jordan, M. (2003).Matching words and pictures. Journal of Machine Learning Research, 3, 1107-1135.
5. Bekkerman, R., & Jeon, J. (2007). Multi-modal clustering for multimedia collections. In CVPR.
6. Berg, T., Berg, A., Edwards, J., & Forsyth, D. (2004). Who's in the picture. In NIPS.
7. Blaschko, M. B., & Lampert, C. H. (2008). Correlational spectral clustering. In CVPR.
8. Bruce, N., & Tsotsos, J. (2005). Saliency based on information maximization. In NIPS.
9. Datta, R., Joshi, D., Li, J., & Wang, J. (2008). Image retrieval: ideas, influences, and trends of the New Age. ACM Computing Surveys, 40(2), 1-60.
10. Deng, J., Dong, W., Socher, R., Li, L. J., Li, K., & Fei-Fei, L. (2009). Imagenet: a large-scale hierarchical image database. In CVPR.
11. Duygulu, P., Barnard, K., de Freitas, N., & Forsyth, D. (2002). Object recognition as machine translation: learning a lexicon for a fixed image vocabulary. In ECCV.
12. Einhauser,W., Spain, M., & Perona, P. (2008). Objects predict fixations better than early saliency. Journal of Vision, 8(14), 1-26.
13. Elazary, L., & Itti, L. (2008). Interesting objects are visually salient. Journal of Vision, 8(3), 1-15.
14. Everingham, M., Van Gool, L., Williams, C. K. I., Winn, J., & Zisserman, A. (2007). The PASCAL visual object classes challenge 2007 (VOC2007) Results. http://www.pascal-network.org/challenges/VOC/voc2007/workshop/index. html.
15. Farhadi, A., Hejrati, M., Sadeghi, A., Young, P., Rashtchian, C., Hockenmaier, J., & Forsyth, D. (2010). Every picture tells a story: generating sentences for images. In ECCV.
16. Fergus, R., Fei-Fei, L., Perona, P., & Zisserman, A. (2005). Learning object categories from Google's image search. In ICCV.
17. Fleiss, J. L. (1971). Measuring nominal scale agreement among many raters. Psychological Bulletin, 76(5), 378-382.
18. Fyfe, C., & Lai, P. (2001). Kernel and nonlinear canonical correlation analysis. International Journal of Neural Systems, 10, 365-374.
19. Gupta, A., & Davis, L. (2008). Beyond nouns: exploiting prepositions and comparative adjectives for learning visual classifiers. In ECCV.
20. Hardoon, D., & Shawe-Taylor, J. (2003). KCCA for different level precision in content-based image retrieval. In Third international workshop on content-based multimedia indexing.
21. Hardoon, D. R., Szedmak, S., & Shawe-Taylor, J. (2004). Canonical correlation analysis: an overview with application to learning methods. Neural Computation, 16(12).
22. Hotelling, H. (1936). Relations between two sets of variants. Biometrika, 28, 321-377.
23. Hwang, S. J., & Grauman, K. (2010a). Accounting for the relative importance of objects in image retrieval. In British machine vision conference.
24. Hwang, S. J., & Grauman, K. (2010b). Reading between the lines: object localization using implicit cues from image tags. In CVPR.
25. Jarvelin, K., & Kekalainen, J. (2002). Cumulated gain-based evaluation of IR techniques. ACM Transactions on Information Systems, 20(4), 422-446. (Pubitemid 44642296)
26. Kadir, T., & Brady, M. (2001). Saliency, scale and image description. International Journal of Computer Vision, 45(2), 83-105. (Pubitemid 33125858)
27. Kulis, B., & Grauman, K. (2009). Kernelized locality-sensitive hashing for scalable image search. In ICCV.
28. Lavrenko, V., Manmatha, R., & Jeon, J. (2003). A model for learning the semantics of pictures. In NIPS.
29. Li, L., Wang, G., & Fei-Fei, L. (2007). Optimol: automatic online picture collection via incremental model learning. In CVPR.
30. Li, L. J., Socher, R., & Fei-Fei, L. (2009). Towards total scene understanding: classification, annotation and segmentation in an automatic framework. In CVPR.
31. Li, Y., & Shawe-Taylor, J. (2006). Using KCCA for Japanese-English cross-language information retrieval and document classification. Journal of Intelligent Information Systems, 27(2).
32. Loeff, N., & Farhadi, A. (2008). Scene discovery by matrix factorization. In ECCV.
33. Lowe, D. (2004). Distinctive image features from scale-invariant keypoints. IJCV, 60(2).
34. Makadia, A., Pavlovic, V., & Kumar, S. (2008). A new baseline for image annotation. In ECCV.
35. Monay, F., & Gatica-Perez, D. (2003). On image auto-annotation with latent space models. In ACM multimedia.
36. Qi, G. J., Hua, X. S., & Zhang, H. J. (2009). Learning semantic distance from community-tagged media collection. In ACM multimedia.
37. Quack, T., Leibe, B., & Gool, L. V. (2008). World-scale mining of objects and events from community photo collections. In CIVR.
38. Quattoni, A., Collins, M., & Darrell, T. (2007). Learning visual representations using images with captions. In CVPR.
39. Russell, B., Torralba, A., Murphy, K., & Freeman, W. (2005). Labelme: a database and web-based tool for image annotation (Tech. rep). MIT.
40. Schroff, F., Criminisi, A., & Zisserman, A. (2007). Harvesting image databases from the web. In ICCV.
41. Smeulders, A., Worring, M., Santini, S., Gupta, A., & Jain, R. (2000). Content-based image retrieval at the end of the early years. IEEE Transactions on Pattern Analysis and Machine Intelligence, 22(12), 1349-1380. (Pubitemid 32088465)
42. Spain, M., & Perona, P. (2008). Some objects are more equal than others: measuring and predicting importance. In ECCV.
43. Tatler, B., Baddeley, R., & Gilchrist, I. (2005). Visual correlates of fixation selection: effects of scale and time. Vision Research, 45, 643-659. (Pubitemid 40037974)
44. Torralba, A. (2003). Contextual priming for object detection. International Journal of Computer Vision, 53(2), 169-191.
45. Vijayanarasimhan, S., & Grauman, K. (2008). Keywords to visual categories: multiple-instance learning for weakly supervised object categorization. In CVPR.
46. Wolfe, J., & Horowitz, T. (2004).What attributes guide the deployment of visual attention and how do they do it? Neuroscience, 5, 495-501. (Pubitemid 38715516)
47. Yakhnenko, O.,& Honavar, V. (2009). Multiple label prediction for image annotation with multiple kernel correlation models. In Workshop on visual context learning, in conjunction with CVPR.