Superimage: Packing semantic-relevant images for indexing and retrieval

ICMR 2014 - Proceedings of the ACM International Conference on Multimedia Retrieval 2014

Volumn , Issue , 2014, Pages 41-48

  Luo, Qingjun ^a   Zhang, Shiliang ^b   Huang, Tiejun ^a   Gao, Wen ^a   Tian, Qi ^b  

^a PEKING UNIVERSITY   (China)

^b One UTSA Circle   (United States)

Author keywords

Image indexing; Large scale visual search

Indexed keywords

IMAGE RETRIEVAL; SEMANTICS;

FUSION ALGORITHMS; IMAGE INDEXING; INDEXING AND RETRIEVAL; LARGE-SCALE DATASET; LOCAL DESCRIPTORS; MEMORY CONSUMPTION; SEMANTIC RELEVANCE; VISUAL SEARCH;

INDEXING (OF INFORMATION);

EID: 84899707509     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1145/2578726.2578741     Document Type: Conference Paper

References (32)

1. Large scale visual recognition challenge. http://www.image-net.org/ challenges/LSVRC/2010, 2010. 2, 3.1
2. J. Deng, A. C. Berg, and L. Fei-Fei. Hierarchical semantic indexing for large scale image retrieval. In CVPR, 2011. 1, 2
3. M. Douze, A. Ramisa, and C. Schmid. Combining attributes and fisher vectors for effcient image retrieval. In CVPR, 2011. 1, 1, 2, 2
4. C. Fellbaum. Wordnet: an electronic lexical database. Bradford Books, 1998. 2
5. B. J. Frey and D. Dueck. Clustering by passing messages between data points. Science, 315(5814):972-976, 2007. 3.2
6. A. Gionis, P. Indyky, and R. Motwaniz. Similarity search in high dimensions via hashing. In International Conference on Very Large Data Bases (VLDB), pages 518-529, 1999. 1
7. M. J. Huiskes and M. S. Lew. The mir flickr retrieval evaluation. In MIR '08: Proceedings of the 2008 ACM ICMIR, New York, NY, USA, 2008. ACM. 5.1
8. H. Jégou, M. Douze, and C. Schmid. Hamming embedding and weak geometric consistency for large scale image search. In ECCV, 2008. 2, 5.1, 2
9. H. Jégou, M. Douze, and C. Schmid. Improving bag-of-feature for large scale image search. IJCV, 87(3):316-336, 2010. 2
10. H. Jégou, M. Douze, and C. Schmid. Product quantization for nearest neighbor search. TPAMI, 33(1):117-128, 2011. 3.2
11. R. M. Karp. Reducibility among combinatorial problems. Springer, 1972. 3.2
12. A. Krizhevsky, I. Sutskever, and G. Hinton. Imagenet classification with deep convolutional neural networks. In NIPS, 2012. 2
13. Z. Liu, H. Li, W. Zhou, and Q. Tian. Embedding spatial context into inverted file for large-scale image search. In ACM Multimedia, 2012. 1, 2, 2
14. D. G. Lowe. Distinctive image features from scale invariant keypoints. IJCV, 60(2):91-110, 2004. 1, 2
15. K. Makino and T. Uno. New algorithms for enumerating all maximal cliques. In Algorithm Theory-SWAT 2004, pages 260-272. Springer, 2004. 1, 3.2
16. T. Mei, Y. Rui, S. Li, and Q. Tian. Multimedia search reranking: A literature survey. ACM Computing Survey, 2013. 1
17. A. Y. Ng, M. I. Jordan, Y. Weiss, et al. On spectral clustering: Analysis and an algorithm. Advances in neural information processing systems, 2:849-856, 2002. 3.2
18. D. Nistér and H. Stewénius. Scalable recognition with a vocabulary tree. In CVPR, 2006. 1, 1, 1, 2, 4.1, 5.1, 5.2, 3
19. F. Perronnin, J. Sánchez, and T. Mensink. Improving the fisher kernel for large-scale image classification. In ECCV, volume 4, pages 143-156, 2010. 1, 2
20. J. Philbin, O. Chum, M. Isard, J. Sivic, and A. Zisserman. Object retrieval with large vocabularies and fast spatial matching. In CVPR, 2007. 2, 2
21. J. Sivic and A. Zisserman. Video google: A text retrieval approach to object matching in videos. In ICCV, 2003. 1, 2
22. E. Tomita, A. Tanaka, and H. Takahashi. The worst-case time complexity for generating all maximal cliques and computational experiments. Theoretical Computer Science, 363(1):28-42, 2006. 1, 3.2
23. A. Torralba, R. Fergus, and Y. Weiss. Small codes and large image databases for recognition. In CVPR, 2008. 1
24. L. Torresani, M. Szummer, and A. Fitzgibbon. Efficient object category recognition using classemes. In ECCV, pages 776-789, 2010. 1, 2, 3.1, 5.3
25. Z. Wu, Q. Ke, M. Isard, and J. Sun. Bundling feature for large scale partial-duplicated web image search. In CVPR, 2009. 1, 2
26. G. Ye, D. Liu, I.-H. Jhuo, and S.-F. Chang. Robust late fusion with rank minimization. In CVPR, 2012. 1, 1, 2
27. S. Zhang, Q. Huang, G. Hua, S. Jiang, W. Gao, and Q. Tian. Building contextual visual vocabulary for large-scale image applications. In ACM Multimedia, 2010. 1, 1
28. S. Zhang, Q. Tian, G. Hua, Q. Huang, and W. Gao. Descriptive visual words and visual phrases for image applications. In ACM Multimedia, 2009. 1, 2
29. S. Zhang, Q. Tian, K. Lu, Q. Huang, and W. Gao. Edge-sift: Discriminative binary descriptor for scalable partial-duplicate mobile search. TIP, 2013. 1
30. S. Zhang, M. Yang, T. Cour, K. Yu, and D. N. Metaxas. Query specific fusion for image retrieval. In ECCV, volume 2, pages 660-673, 2012. 1, 1, 2
31. S. Zhang, M. Yang, X. Wang, Y. Lin, and Q. Tian. Sematnic-aware co-indexing for image retrieval. In ICCV, 2013. 2, 3.1
32. Y. Zhang, Z. Jia, and T. Chen. Image retrieval with geometry-preserving visual phrases. In CVPR, 2011. 1, 2, 2