Efficiently matching sets of features with random histograms

MM'08 - Proceedings of the 2008 ACM International Conference on Multimedia, with co-located Symposium and Workshops

Volumn , Issue , 2008, Pages 179-188

  Dong, Wei ^a   Wang, Zhe ^a   Charikar, Moses ^a   Li, Kai ^a  

^a Princeton University   (United States)

Author keywords

Locality sensitive hashing; Random histogram; Set of features

Indexed keywords

APPROXIMATE FEATURES; AUXILIARY SPACE; CALTECH; CONTENT-BASED; CONTENT-BASED SEARCH; DATA OBJECTS; DATA SETS; DISTANCE MEASURE; FEATURE VECTORS; HIGH-DIMENSIONAL; IMAGE OBJECT RECOGNITION; IMAGE SEARCH; IN-BUILDINGS; LOCALITY SENSITIVE HASHING; MASSIVE DATA SETS; RANDOM HISTOGRAM; RANDOMIZED ALGORITHMS; SET MATCHING; SET OF FEATURES; SIMILARITY MEASURE; SPACE AND TIME; VIDEO CLIPS;

IMAGE RETRIEVAL; METHOD OF MOMENTS; MULTIMEDIA SYSTEMS; SEARCH ENGINES; VECTORS; WORLD WIDE WEB;

OBJECT RECOGNITION;

EID: 70350637634     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1145/1459359.1459384     Document Type: Conference Paper

References (30)

1. http://www.cs.princeton.edu/cass.
2. A. Z. Broder, M. Charikar, A. M. Frieze, and M. Mitzenmacher. Min-wise independent permutations (extended abstract). In STOC '98: Proceedings of the thirtieth annual ACM symposium on Theory of computing, pages 327-336, New York, NY, USA, 1998. ACM.
3. C.-C. Chang and C.-J. Lin. LIBSVM: a library for support vector machines, 2001. Software available at http://www.csie.ntu.edu.tw/̃cjlin/libsvm.
4. M. S. Charikar. Similarity estimation techniques from rounding algorithms. In STOC '02: Proceedings of the thiry-fourth annual ACM symposium on Theory of computing, pages 380-388, New York, NY, USA, 2002. ACM.
5. M. Datar, N. Immorlica, P. Indyk, and V. S. Mirrokni. Locality-sensitive hashing scheme based on p-stable distributions. In SCG '04: Proceedings of the twentieth annual symposium on Computational geometry, pages 253-262, New York, NY, USA, 2004. ACM.
6. Y. Deng and B. S. Manjunath. Unsupervised segmentation of color-texture regions in images and video. IEEE Trans. on Pattern Analysis and Machine Intelligence, 23(8):800-810, 2001.
7. W. Dong, M. Charikar, and K. Li. High dimensional similarity search with sketches. In Proceedings of the 31st Annual International ACM SIGIR Conference on Research & Development on Information Retrieval, Singapore, July 2008.
8. L. Fei-Fei, R. Fergus, and P. Perona. Learning generative visual models from few training examples: An incremental bayesian approach tested on 101 object categories. Computer Vision and Image Understanding, In Press, Corrected Proof.
9. A. Gionis, P. Indyk, and R. Motwani. Similarity search in high dimensions via hashing. In VLDB '99: Proceedings of the 25th International Conference on Very Large Data Bases, pages 518-529, San Francisco, CA, USA, 1999. Morgan Kaufmann Publishers Inc.
10. K. Grauman and T. Darrell. Pyramid match hashing: Sub-linear time indexing over partial correspondences. Computer Vision and Pattern Recognition, 2007. CVPR '07. IEEE Conference on, pages 1-8, 17-22 June 2007.
11. K. Grauman and T. Darrell. The pyramid match kernel: Discriminative classification with sets of image features. In ICCV '05: Proceedings of the Tenth IEEE International Conference on Computer Vision, pages 1458-1465, Washington, DC, USA, 2005. IEEE Computer Society.
12. K. Grauman and T. Darrell. Approximate correspondences in high dimensions. In B. Schölkopf, J. Platt, and T. Hoffman, editors, Advances in Neural Information Processing Systems 19, pages 505-512. MIT Press, Cambridge, MA, 2007.
13. K. Grauman and T. Darrell. The pyramid match kernel: Efficient learning with sets of features. J. Mach. Learn. Res., 8:725-760, 2007.
14. P. Indyk and R. Motwani. Approximate nearest neighbors: towards removing the curse of dimensionality. In STOC '98: Proceedings of the thirtieth annual ACM symposium on Theory of computing, pages 604-613, New York, NY, USA, 1998. ACM.
15. P. Indyk and N. Thaper. Fast image retrieval via embeddings. In 3rd International Workshop on Statistical and Computational Theories of Vision, 2003.
16. T. Joachims. Training linear svms in linear time. In KDD '06: Proceedings of the 12th ACM SIGKDD international conference on Knowledge discovery and data mining, pages 217-226, New York, NY, USA, 2006. ACM.
17. Y. Ke and R. Sukthankar. Pca-sift: A more distinctive representation for local image descriptors. cvpr, 02:506-513, 2004.
18. A. Kirsch and M. Mitzenmacher. Distance-sensitive bloom filters. In ALENEX '06: Proceedings of the Ninth Workshop on Algorithm Engineering and Experiments, pages 41-50, Philadelphia, PA, USA, 2006. Society for Industrial and Applied Mathematics.
19. S. Lazebnik, C. Schmid, and J. Ponce. Beyond bags of features: Spatial pyramid matching for recognizing natural scene categories. In CVPR '06: Proceedings of the 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, pages 2169-2178, Washington, DC, USA, 2006. IEEE Computer Society.
20. B. Leibe and B. Schiele. Analyzing appearance and contour based methods for object categorization. cvpr, 02:409, 2003.
21. D. G. Lowe. Distinctive image features from scale-invariant keypoints. Int. J. Comput. Vision, 60(2):91-110, 2004.
22. Q. Lv, M. Charikar, and K. Li. Image similarity search with compact data structures. In CIKM '04: Proceedings of the thirteenth ACM international conference on Information and knowledge management, pages 208-217, New York, NY, USA, 2004. ACM.
23. Q. Lv, W. Josephson, Z. Wang, M. Charikar, and K. Li. Ferret: a toolkit for content-based similarity search of feature-rich data. In EuroSys '06: Proceedings of the 1st ACM SIGOPS/EuroSys European Conference on Computer Systems 2006, pages 317-330, New York, NY, USA, 2006. ACM.
24. S. Lyu. A kernel between unordered sets of data: The gaussian mixture approach. In European Conference on Machine Learning (ECML), Porto, Portugal, 2005.
25. S. Lyu. Mercer kernels for object recognition with local features. In CVPR '05: Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Volume 2, pages 223-229, Washington, DC, USA, 2005. IEEE Computer Society.
26. A. Opelt, M. Fussenegger, A. Pinz, and P. Auer. Weak Hypotheses and Boosting for Generic Object Detection and Recognition, volume 3022/2004 of Lecture Notes in Computer Science, pages 71-84. Springer Berlin / Heidelberg, 2004.
27. Y. Rubner, C. Tomasi, and L. J. Guibas. The earth mover's distance as a metric for image retrieval. Int. J. Comput. Vision, 40(2):99-121, 2000.
28. J. Z. Wang, J. Li, and G. Wiederhold. Simplicity: Semantics-sensitive integrated matching for picture libraries. IEEE Trans. Pattern Anal. Mach. Intell., 23(9):947-963, 2001.
29. X. Wu, A. G. Hauptmann, and C.-W. Ngo. Practical elimination of near-duplicates from web video search. In MULTIMEDIA '07: Proceedings of the 15th international conference on Multimedia, pages 218-227, New York, NY, USA, 2007. ACM.
30. J. Zhang, M. Marsza lek, S. Lazebnik, and C. Schmid. Local features and kernels for classification of texture and object categories: A comprehensive study. Int. J. Comput. Vision, 73(2):213-238, 2007.