Image specificity

Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition

Volumn 07-12-June-2015, Issue , 2015, Pages 2727-2736

  Jas, Mainak ^a   Parikh, Devi ^b  

^a AALTO UNIVERSITY   (Finland)

^b VIRGINIA POLYTECHNIC INSTITUTE AND STATE UNIVERSITY   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

COMPUTER VISION; INFORMATION RETRIEVAL; PATTERN RECOGNITION;

IMAGE CONTENT; IMAGE FEATURES; MODEL IMAGES; MULTIPLE DESCRIPTIONS; MULTIPLE PEOPLE; REFERENCE DESCRIPTIONS; TEXT-BASED IMAGE RETRIEVALS; TEXTUAL DESCRIPTION;

IMAGE RETRIEVAL;

EID: 84959214146     PISSN: 10636919     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/CVPR.2015.7298889     Document Type: Conference Paper

References (53)

1. S. Banerjee and A. Lavie. METEOR: An automatic metric for MT evaluation with improved correlation with human judgments. 2005
2. K. Barnard and D. Forsyth. Learning the semantics of words and pictures. In IEEE International Conference on Computer Vision (ICCV), volume 2, pages 408-4 1 5, 200 1
3. A. C. Berg, T. L. Berg, H. Daume, J. Dodge, A. Goyal, X. Han, A. Mensch, M. Mitchell, A. Sood, K. Stratos, et al. Understanding and predicting importance in images. In IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pages 3 5 62-3569, 2012.
4. S. Bird. NLTK: the natural language toolkit. In P roceedings of the COLINGIACL on Interactive presentation sessions, pages 69-72. Association for Computational Linguistics, 2006
5. X. Chen and e. L. Zitnick. Learning a Recurrent Visual Representation for Image Caption Generation. In IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2015
6. J. Cui, F. Wen, and X. Tang. Real time google and live image search re-ranking. In Proceedings of the 16th ACM international conference on Multimedia, pages 729-732. ACM, 2008
7. S. Dhar, V. Ordonez, and T. L. Berg. High level describable attributes for predicting aesthetics and interestingness. In 1EEE Conference on Computer Vision and Pattern Recognition (CVPR), pages 1 657-1 664, 2011
8. J. Donahue, L. A. Hendricks, S. Guadarrama, M. Rohrbach, S. Venugopalan, K. Saenko, and T. Darrell. Long-term recurrent convolutional networks for visual recognition and description. arXiv preprint arXiv:1411. 4389, 20 14
9. J. Donahue, Y. Jia, O. Vinyals, J. Hoffman, N. Zhang, E. Tzeng, and T. Darrell. Decaf: A deep convolutional activation feature for generic visual recognition. arXiv preprint arXiv:1310. 1531, 2013
10. M. Douze, A. Ramisa, and C. Schmid. Combining attributes and fisher vectors for efficient image retrieval. In 1EEE Conference on Computer Vision and Pattern Recognition (CVPR), pages 745-752, 201 1
11. D. Elliott and F. Keller. Comparing Automatic Evaluation Measures for Image Description
12. M. Everingham, S. A. Eslami, L. Van Gool, e. K. Williams, J. Winn, and A. Zisserman. The Pascal Visual Object Classes Challenge-a Retrospective
13. H. Fang, S. Gupta, F. landola, R. Srivastava, L. Deng, P. Dollar, J. Gao, X. He, M. Mitchell, J. Platt, et al. From captions to visual concepts and back. In IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2015
14. A. Farhadi, M. Hejrati, M. A. Sadeghi, P. Young, e. Rashtchian, J. Hockenmaier, and D. Forsyth. Every picture tells a story: Generating sentences from images. In Computer Vision-ECCV 2010, pages 1 5-29. Springer, 2010.
15. J. Freeman and E. P. Simoncelli. Metamers of the ventral stream. Nature Neuroscience, 1 4(9): 1 1 95-1 201, 2011
16. R. Girshick, J. Donahue, T. Darrell, and J. Malik. Rich feature hierarchies for accurate object detection and semantic segmentation. arXiv preprint arXiv:1311. 2524, 2013
17. M. Gygli, H. Grabner, H. Riemenschneider, F. Nater, and L. V. Gool. The interestingness of images. In 1EEE International Conference on Computer Vision (ICCV), pages 1 63 3-1 640, 2013
18. P. Isola, D. Parikh, A. Torralba, and A. Oliva. Understanding the intrinsic memorability of images. In Advances in Neural Information Processing Systems, 2011
19. P. Isola, J. Xiao, A. Torralba, and A. Oliva. What makes an image memorable? In IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pages 1 45-1 52, 2011
20. L. Itti, e. Koch, and E. Niebur. A model of saliency-based visual attention for rapid scene analysis. IEEE Transactions on pattern analysis and machine intelligence, 20( 1 1): 1 254-1 259, 1 99 8
21. M. Jas and D. Parikh. Image Specificity. arXiv preprint arXiv:1502. 04569, 2015.
22. T. Judd, K. Ehinger, F. Durand, and A. Torralba. Learning to Predict Where Humans Look. In IEEE International Conference on Computer Vision (ICCV), 2009
23. A. Karpathy and L. Fei-Fei. Deep visual-semantic alignments for generating image descriptions. In IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2015
24. A. Khosla, A. Das Sarma, and R. Hamid. What makes an image popular? In P roceedings of the 23rd international conference on World wide web, pages 867-876. International World Wide Web Conferences Steering Committee, 20 14
25. R. Kiros, R. Salakhutdinov, and R. S. Zemel. Unifying visual-semantic embeddings with multi modal neural language models. In Transactions of the Association for Computational Linguistics (TACL), 2015
26. A. Krizhevsky, l. Sutskever, and G. E. Hinton. Imagenet classification with deep convolutional neural networks. In Advances in Neural1nformation P rocessing Systems, pages 1 097-1 1 05, 2012
27. R. Krovetz and W. B. Croft. Lexical ambiguity and information retrieval. ACM Transactions on Information Systems (T01S), 1 0(2): 1 1 5-1 4 1, 1 992
28. G. Kulkarni, V. Premraj, S. Dhar, S. Li, Y. Choi, A. e. Berg, and T. L. Berg. Baby talk: Understanding and generating simple image descriptions. In IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pages 1 60 1-1 608, 2011.
29. M. S. Lew. Next-generation web searches for visual content. Computer, 3 3 ( 1 1): 46-5 3, 2000
30. T. Leyvand, D. Cohen-Or, G. Dror, and D. Lischinski. Datadriven enhancement of facial attractiveness. ACM Transactions on Graphics (TOG), 27(3): 3 8, 2008
31. S. Li, G. Kulkarni, T. L. Berg, A. C. Berg, and Y. Choi. Composing simple image descriptions using web-scale n-grams. In P roceedings of the Fifteenth Conference on Computational Natural Language Learning, pages 220-228. Association for Computational Linguistics, 201 1
32. C.-Y. Lin. Rouge: A package for automatic evaluation of summaries. In Text Summarization Branches Out: P roceedings of the ACL-04 Workshop, pages 74-8 1, 2004.
33. D. Lin. An information-theoretic definition of similarity
34. D. Lin, S. Fidler, C. Kong, and R. Urtasun. Visual semantic search: Retrieving videos via complex textual queries. In Computer Vision and Pattern Recognition (CVPR), 2014 IEEE Conference on, pages 2657-2664, 20 14
35. J. Mao, W Xu, Y. Yang, J. Wang, and A. L. Yuille. Explain images with multi modal recurrent neural networks. arXiv preprint arXiv: 1410. 1090, 2014
36. G. A. Miller. WordNet: a lexical database for English. Communications of the ACM, 3 8 ( 1 1): 3 9-4 1, 1 995. 3, 6
37. M. Mitchell, X. Han, J. Dodge, A. Mensch, A. Goyal, A. Berg, K. Yamaguchi, T. Berg, K. Stratos, and H. Daume III. Midge: Generating image descriptions from computer vision detections. In P roceedings of the 13th Conference of the European Chapter of the Association for Computational Linguistics, pages 747-756. Association for Computational Linguistics, 2012.
38. V. Ordonez, G. Kulkarni, and T. L. Berg. Im2text: Describing images using 1 million captioned photographs. In Advances in Neural Information Processing Systems, pages 1 1 43-1 1 5 1, 201 1.
39. K. Papineni, S. Roukos, T. Ward, and W-J. Zhu. BLEU: a method for automatic evaluation of machine translation. In Proceedings of the 40th annual meeting on association for computational linguistics, pages 3 1 1-3 1 8. Association for Computational Linguistics, 2002.
40. F. Pedregosa, G. Varoquaux, A. Gramfort, V. Michel, B. Thirion, O. Grisel, M. Blondel, P. Prettenhofer, R. Weiss, V. Dubourg, et al. Sci kit-learn: Machine learning in Python. The Journal of Machine Learning Research, 1 2: 2825-2830, 2011
41. c. Rashtchian, P. Young, M. Hodosh, and J. Hockenmaier. Collecting image annotations using Amazon' s Mechanical Turk. In P roceedings of the NAACL HLT 2010 Workshop on Creating Speech and Language Data with Amazon 's Mechanical Turk, pages 1 3 9-1 47. Association for Computational Linguistics, 2010
42. R. Rosenholtz, Y. Li, and L. Nakano. Measuring visual clutter. Journal of vision, 7(2): 1 7, 2007
43. B. Siddiquie, R. S. Feris, and L. S. Davis. Image ranking and retrieval based on multi-attribute queries. In IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pages 8 0 1-808, 201 1
44. M. Spain and P. Perona. Measuring and predicting importance of objects in our visual world. 2007
45. N. Turakhia and D. Parikh. Attribute dominance: What pops out? In IEEE International Conference on Computer Vision (ICCV), pages 1 225-1232, 2013
46. R. Vedantam, C. L. Zitnick, and D. Parikh. CIDEr: Consensus-based Image Description Evaluation. In IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2015
47. O. Vinyals, A. Toshev, S. Bengio, and D. Erhan. Show and tell: A neural image caption generator. In IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2015
48. G. Wang and D. Forsyth. Joint learning of visual attributes, object classes and visual saliency. In Computer Vision, 2009 IEEE 12th International Conference on, pages 537-544, 2009
49. X. Wang, K. Liu, and X. Tang. Query-specific visual semantic spaces for web image re-ranking. In IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pages 857-8 64, 2011
50. J. Xiao, J. Hays, K. A. Ehinger, A. Oliva, and A. Torralba. Sun database: Large-scale scene recognition from abbey to zoo. In IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pages 3485-3492, 2010
51. B. Z. Yao, X. Yang, L. Lin, M. W Lee, and S.-c. Zhu. I2t: Image parsing to text description. P roceedings of the IEEE, 98(8): 1 48 5-1 5 08, 2010
52. B. Zhou, A. Lapedriza, J. Xiao, A. Torralba, and A. Oliva. Learning Deep Features for Scene Recognition using Places Database. NIPS, 2014
53. c. L. Zitnick and D. Parikh. Bringing semantics into focus using visual abstraction. In IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pages 3009-3 0 1 6, 2013 .