Representations of keypoint-based semantic concept detection: A comprehensive study

IEEE Transactions on Multimedia

Volumn 12, Issue 1, 2010, Pages 42-53

  Jiang, Yu Gang ^a,b   Yang, Jun ^c   Ngo, Chong Wah ^a   Hauptmann, Alexander G ^d  

^a CITY UNIVERSITY OF HONG KONG   (Hong Kong)

^b Department of Earth and Environmental Sciences   (United States)

^c GOOGLE INC   (United States)

^d Carnegie Mellon University ^*  (United States)

Author keywords

Bag of visual words; Representation choice; Semantic concept detection

Indexed keywords

BAG-OF-VISUAL-WORDS; BENCHMARK DATA; COMPREHENSIVE STUDIES; CONCEPT DETECTION; DATA SETS; EMPIRICAL FINDINGS; FEATURE SELECTION; IMAGE PATCHES; KEYPOINTS; MULTIMEDIA COMMUNITY; MULTIMEDIA DATABASE; SCENE CLASSIFICATION; SEMANTIC CONCEPT; SEMANTIC CONCEPT DETECTION; SPATIAL INFORMATIONS; STOP WORD; TRECVID; VISUAL WORD; VOCABULARY SIZE; WEIGHTING METHODS; WEIGHTING SCHEME;

DETECTORS; FEATURE EXTRACTION; IMAGE PROCESSING; MULTIMEDIA SYSTEMS; SEMANTICS;

OBJECT RECOGNITION;

EID: 72949121298     PISSN: 15209210     EISSN: None     Source Type: Journal    
DOI: 10.1109/TMM.2009.2036235     Document Type: Article

References (46)

1. J. Sivic and A. Zisserman, "Video google: A text retrieval approach to object matching in videos," in Proc. ICCV, 2003.
2. Y. G. Jiang, C. W. Ngo, and J. Yang, "Towards optimal bag-of-features for object categorization and semantic video retrieval," in Proc. ACM CIVR, 2007.
3. S. Lazebnik, C. Schmid, and J. Ponce, "Beyond bags of features: Spatial pyramid matching for recognizing natural scene categories," in Proc. CVPR, 2006.
4. E. Nowak, F. Jurie, and B. Triggs, "Sampling strategies for bag-offeatures image classification," in Proc. ECCV, 2006.
5. J. Zhang, M. Marszalek, S. Lazebnik, and C. Schmid, "Local features and kernels for classification of texture and object categories: A comprehensive study," Int. J. Comput. Vis., vol.73, no.2, pp. 213-238, 2007.
6. C. G. M. Snoek et al., "The MediaMill TRECVID 2008 semantic video search engine," in Proc. TRECVID Workshop, 2008.
7. "LSCOM lexicon definitions and annotations," in DTO Challenge Workshop on Large Scale Concept Ontology for Multimedia, Columbia Univ. ADVENT Tech. Rep. 217-2006-2013, 2006.
8. A. Yanagawa, S.-F. Chang, L. Kennedy, and W. Hsu, "Columbia University's Baseline Detectors for 374 LSCOM Semantic Visual Concepts," Columbia Univ. Advent Tech. Rep., 2007.
9. C. G. M. Snoek, M.Worring, J. C. van Gemert, J.-M. Geusebroek, and A. W. M. Smeulders, "The challenge problem for automated detection of 101 semantic concepts in multimedia," in Proc. ACM Multimedia, 2006.
10. A. F. Smeaton, P. Over, and W. Kraaij, "Evaluation campaigns and TRECVID," in Proc. ACM MIR, 2006.
11. J. Cao et al., "Intelligent multimedia group of Tsinghua University at TRECVID 2006," in Proc. TRECVID Workshop, 2006.
12. S.-F. Chang et al., "Columbia university TRECVID-2006 video search and high-level feature extraction," in Proc. TRECVID Workshop, 2006.
13. M. Campbell et al., "IBM research TRECVID-2006 video retrieval system," in Proc. TRECVID Workshop, 2006.
14. A. G. Hauptmann et al., "Multi-lingual broadcast news retrieval," in Proc. TRECVID Workshop, 2006.
15. C. G. M. Snoek et al., "The Mediamill TRECVID 2006 semantic video search engine," in Proc. TRECVID Workshop, 2006.
16. K. E. Van de Sande, T. Gevers, and C. G. M. Snoek, "A comparison of color features for visual concept classification," in Proc. ACM CIVR, 2008.
17. J. Yang, Y. G. Jiang, A. G. Hauptmann, and C. W. Ngo, "Evaluating bag-of-visual-words representations in scene classification," in Proc. ACM MIR, 2007.
18. D. Lowe, "Distinctive image features from scale-invariant keypoints," Int. J. Comput. Vis., vol.60, no.2, pp. 91-110, 2004.
19. J. Van de Weijer and C. Schmid, "Coloring local feature extraction," in Proc. ECCV, 2006.
20. S. Petrov et al., "Detecting categories in news video using acoustic, speech, and image features," in Proc. TRECVID Workshop, 2006.
21. A. C. Berg and J. Malik, "Geometric blur for template matching," in Proc. CVPR, 2001.
22. K. Grauman and T. Darrell, "The pyramid match kernel: Discriminative classification with sets of image features," in Proc. ICCV, 2005.
23. J. Philbin, O. Chum, M. Isard, J. Sivic, and A. Zisserman, "Lost in quantization: Improving particular object retrieval in large scale image databases," in Proc. CVPR, 2008.
24. J. C. van Gemert, C. J.Veenman, A. W. M. Smeulders, and J. M. Geusebroek, "Visual word ambiguity," IEEE Trans. Pattern Anal. Mach. Intell., accepted for publication.
25. K. Mikolajczyk and C. Schmid, "Scale and affine invariant interest point detectors," Int. J. Comput. Vis., vol.60, pp. 63-86, 2004.
26. K. Mikolajczyk and C. Schmid, "A performance evaluation of local descriptors," IEEE Trans. Pattern Anal. Mach. Intell., vol.27, no.10, pp. 1615-1630, Oct. 2005.
27. Y. Yang and X. Liu, "A comparative study on feature selection in text categorization," in Proc. ICML, 1997.
28. W. B. Cavnar and J. M. Trenkle, "N-gram based text categorization," in Proc. 3rd Annu. Symp. Document Analysis and Information Retrieval, 1994.
29. C. M. Tan, Y. F. Wang, and C. D. Lee, "The use of bigrams to enhance text categorization," J. Inf. Process. Manage., vol.30, no.4, pp. 529-546, 2002.
30. S. Lazebnik, C. Schmid, and J. Ponce, "A maximum entropy framework for part-based texture and object recognition," in Proc. IEEE ICCV, 2005.
31. S. Nowozin, K. Tsuda, T. Uno, T. Kudo, and G. Bakir, "Weighted substructure mining for image analysis," in Proc. CVPR, 2007.
32. F. Odone, A. Barla, and A. Verri, "Building kernels from binary strings for image matching," IEEE Trans. Image Process., vol.14, no.2, pp. 169-180, Feb. 2005.
33. C. Fowlkes, S. Belongie, F. Chung, and J. Malik, "Spectral grouping using the Nystrom method," IEEE Trans. Pattern Anal. Mach. Intell., vol.26, no.2, pp. 214-225, Feb. 2004.
34. O. Chapelle, P. Haffner, and V. N. Vapnik, "Support vector machines for histogram-based image classification," IEEE Trans. Neural Netw., vol.10, no.5, pp. 1055-1064, Sep. 1999.
35. V. N. Vapnik, The Nature of Statistical Learning Theory. New York: Springer-Verlag, 1995.
36. TREC Video Retrieval Evaluation (TRECVID). [Online]. Available: http://www-nlpir.nist.gov/projects/trecvid/
37. C. C. Chang and C. J. Lin, LIBSVM: A Library for Support Vector Machines, 2001. [Online]. Available:http://www.csie.ntu.edu.tw/cjlin/libsvm
38. J. Platt, "Probabilities for SV machines," Adv. Large Marg. Classif., pp. 61-74, 2000.
39. M. Everingham, L. Van Gool, C. K. I. Williams, J. Winn, and A. Zisserman, The PASCAL Visual Object Classes Challenge 2007 (VOC2007) Results. [Online]. Available: http://www.pascal-network.org/challenges/VOC/voc2007/workshop/index. html
40. K. Mikolajczyk, T. Tuytelaars, C. Schmid, A. Zisserman, and J. Matas, "A comparison of affine region detectors," Int. J. Comput. Vis., vol. 65, no. 1/2, pp. 43-72, 2005.
41. S. F. Chang et al., "Columbia University/VIREO-cityU/IRIT TRECVID2008 high-level feature extraction and interactive video search," in Proc. TRECVID Workshop, 2008.
42. X.-Y.Wei and C.-W. Ngo, "Fusing semantics, observability, reliability and diversity of concept detectors for video search," in Proc. ACM Multimedia, 2008.
43. R. Aly, D. Hiemstra, A. de Vries, and F. de Jong, "A probabilistic ranking framework using unobservable binary events for video search," in Proc. ACM CIVR, 2008.
44. S. Tang et al., "TRECVID 2008 participation by MCG-ICT-CAS," in Proc. TRECVID Workshop, 2008.
45. J. Sun et al., "Hierarchical spatio-temporal context modeling for action recognition," in Proc. CVPR, 2009.
46. F. Wang, Y.-G. Jiang, and C.-W. Ngo, "Video event detection using motion relativity and visual relatedness," in Proc. ACM Multimedia, 2008.