Defining semantic structure features for content-based visual object class recognition

Journal of Imaging Science and Technology

Volumn 55, Issue 2, 2011, Pages

  Ahmad, Nishat ^a   Park, Jongan ^a  

^a Chosun University   (South Korea)

Author keywords

[No Author keywords available]

Indexed keywords

BENCHMARK DATABASE; BOTTOM UP APPROACH; CLOSE PROXIMITY; CONTENT-BASED; FEATURE VECTORS; GEOMETRICAL PROPERTY; GESTALT LAW; GROUP MEMBERS; HUMAN VISION; IN-LINE; K-NEAREST NEIGHBORS; LINE SEGMENT; MINIMUM DISTANCE; OBJECT CLASS RECOGNITION; SCALE-INVARIANT; SEMANTIC RELATIONS; SEMANTIC STRUCTURES; VISUAL OBJECTS;

ALGORITHMS; GEOMETRY;

SEMANTICS;

EID: 84867210070     PISSN: 10623701     EISSN: 19433522     Source Type: Journal    
DOI: 10.2352/J.ImagingSci.Technol.2011.55.2.020509     Document Type: Conference Paper

References (42)

1. J. Ponce, M. Hebert, C. Schmid, and A. Zisserman, LNCS (Springer- Verlag, Berlin, 2006), Vol. 4170.
2. E. Rosch, C. B. Mervis, W. D. Gray, D. M. Johnson, and P. Boyes-Braem, "Basic objects in natural categories", Cogn. Psychol. 8, 382-439 (1976).
3. S. Ullman and E. Sali, LNCS (Springer-Verlag, Berlin, 2000), Vol. 1811, pp. 73-87.
4. J. P. Eakins and M. E. Graham, Content-Based Image Retrieval-A Report to the JISC Technology Applications Programme (University of Northumbria, Newcastle, 1999).
5. V. N. Gudivada and V. V. Raghavan, "Content-based image retrieval systems", IEEE Comput. 28, 18 (1995).
6. J. P. Eakins, "Towards intelligent image retrieval", Pattern Recogn. 35, 3 (2002).
7. M. S. Lew, N. Sebe, C. Djeraba, and R. Jain, "Content-based multimedia information retrieval: State of the art and challenges", ACM Tran. Multimedia Comput. Commun. Appl. (TOMCCAP) 2, 1 (2006).
8. A. Mojsilovic and B. Rogowitz, "Capturing image semantics with lowlevel descriptors", Proc. Int. Conf. on Image Processing (ICIP) (IEEE, Piscataway, NJ, 2001), pp. 18-21.
9. T. Serre, L. Wolf, and T. Poggio, "Object recognition with features inspired by visual cortex", Proc. Int. Conf. on Computer Vision and Pattern Recognition (CVPR) (IEEE, Piscataway, NJ, 2005), Vol. 2, pp. 994-1000.
10. J. Mundy, LNCS (Springer-Verlag, Berlin, 2006), Vol. 4170, pp. 3-29.
11. D. G. Lowe, "Distinctive image features from scale-invariant keypoints", Int. J. Comput. Vis. 60, 91 (2004).
12. D. G. Lowe, "Object recognition from local scale-invariant features", Proc. Int. Conf. on Computer Vision (ICCV) (IEEE, Piscataway, NJ, 1999), Vol. 2, pp. 1150-1157.
13. A. Teynor, "Patch based approaches for the recognition of visual object classes-A survey", Internal Report 2/06, University of Freiburg (2006) (http://lmb.informatik.uni-freiburg.de/people/teynor/index.en.html).
14. C. Schmid, R. Mohr, and C. Bauckhage, "Evaluation of interest point detectors", Int. J. Comput. Vis. 37, 151 (2000).
15. N. Sebe, Qi Tian, E. Loupias, M. S. Lew, and T. S. Huang, "Evaluation of salient point techniques", Image Vis. Comput. 21, 1087 (2003).
16. K. Mikolajczyk and C. Schmid, "Scale and affine invariant interest point detectors", Int. J. Comput. Vis. 60, 63 (2004).
17. K. Mikolajczyk and C. Schmid, "A performance evaluation of local descriptors", IEEE Trans. Pattern Anal. Mach. Intell. 27, 1615 (2005).
18. K. Mikolajczyk, T. Tuytelaars, C. Schmid, A. Zisserman, J. Matas, F. Schaffalitzky, T. Kadir, and L. Van Gool, "A comparison of affine region detectors", Int. J. Comput. Vis. 65, 43-72 (2005).
19. J. Mutch and D. G. Lowe, "Object class recognition and localization using sparse features with limited receptive fields", Int. J. Comput. Vis. 80, 45-57 (2008).
20. M. Wertheimer and D. King, Max Wertheimer and Gestalt Theory (Transaction Publishers, London, 2004).
21. A. P. Witkin and J. M. Tenenbaum, "What is perceptual organization for?", Proc. 8th Int. Joint Conference on Artificial Intelligence (IJCAI) (International Joint Conferences on Artificial Intelligence, 1983), Vol. 2, pp. 1023-1026.
22. D. G. Lowe, Perceptual Organization and Visual Recognition, Springer International Series in Engineering and Computer Science (Springer- Verlag, Berlin, 1985), Vol. 5.
23. S. Sarkar and K. L. Boyer, "Perceptual organization in computer vision: A review and a proposal for a classificatory structure", IEEE Trans. Syst. Man Cybern. 23, 382-399 (1993).
24. D. Marr, Vision: A Computational Investigation into the Human Representation and Processing of Visual Information (Freeman, New York, 1982).
25. A. Etemadi, J. P. Schmidt, G. Matas, J. Illingworth, and J. V. Kittler, "Low-level grouping of straight line segments", Proc. British Machine Vision Conference (BMVC91) (British Machine Vision Association, Turing Institute, Glasgow, 1991), pp. 119-126.
26. H. Q. Lu and J. K. Aggarwal, "Applying perceptual organization to the detection of man-made objects in non-urban scenes", Pattern Recogn. 25, 835-853 (1992).
27. Q. Iqbal and J. K. Aggarwal, "Retrieval by Classification of images containing large manmade objects using perceptual grouping", Pattern Recogn. 35, 1463-1479 (2002).
28. P. D. Kovesi, "Edges are not just steps", Proc. Fifth Asian Conference on Computer Vision (ACCV) (Australian Pattern Recognition Society, Australia, 2002), pp. 822-827.
29. R. Pope and D. G. Lowe, "VISTA: A software environment for computer vision research", Proc. Int. Conference on Computer Vision and Pattern Recognition (CVPR) (IEEE, Piscataway, NJ, 1994), pp. 768-772.
30. J. Dattorro, Convex Optimization and Euclidean Distance Geometry (Meboo, 2004) (http://meboo.convexoptimization.com/).
31. T. Cover and P. Hart, "Nearest neighbor pattern classification" , IEEE Trans. Inf. Theory 13, 21 (1967).
32. 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", Proc. Int. Conference on Computer Vision and Pattern Recognition (CVPR) (IEEE, Piscataway, NJ, 2004), p. 178.
33. R. Fergus, P. Perona, and A. Zisserman, "Object class recognition by unsupervised scale-invariant learning", Proc. Int. Conference on Computer Vision and Pattern Recognition (CVPR) (IEEE, Piscataway, NJ, 2003), Vol. 2, p. 264.
34. F. Li, J. Kosecka, and H. Wechsler, "Strangeness-based feature selection for part-based recognition", Proc. Int. Conference on Computer Vision and Pattern Recognition (CVPR) (IEEE, Piscataway, NJ, 2006), p. 22.
35. K. Grauman and T. Darrell, "Pyramid match kernels: Discriminative classification with sets of image features", Technical Report MIT-CSAILTR- 2006-020, MIT (2006).
36. H. Zhang, A. Berg, M. Maire, and J. Malik, "SVM-KNN: Discriminative nearest neighbor classification for visual category recognition", Proc. Int. Conference on Computer Vision and Pattern Recognition (CVPR) (IEEE, Piscataway, NJ, 2006), p. 2126.
37. S. Lazebnik, C. Schmid, and J. Ponce, "Beyond bags of features: Spatial pyramid matching for recognizing natural scene categories", Proc. Int. Conference on Computer Vision and Pattern Recognition (CVPR) (IEEE, Piscataway, NJ, 2006), p. 2169.
38. C. Berg, "Shape matching and object recognition", Ph.D. thesis, Computer Science Division, University of California, 2005.
39. C. Berg, T. L. Berg, and J. Malik, "Shape matching and object recognition using low distortion correspondence", Proc. Int. Conference on Computer Vision and Pattern Recognition (CVPR) (IEEE, Piscataway, NJ, 2005), p. 26.
40. G. Wang, Y. Zhang, and L. Fei-Fei, "Using dependent regions for object categorization in a generative framework", Proc. Int. Conference on Computer Vision and Pattern Recognition (CVPR) (IEEE, Piscataway, NJ, 2006), p. 1597.
41. A. Holub, M. Welling, and P. Perona, "Exploiting unlabelled data for hybrid object classification", Proc. NIPS Workshop on Inter-Class Transfer (Neural Information Processing Systems Foundation, La Jolla, CA, 2005).
42. O. Boiman, E. Shechtman, and M. Irani, "In defense of nearest neighbor-based image classification", Proc. Int. Conference on Computer Vision and Pattern Recognition (CVPR) (IEEE, Piscataway, NJ, 2008), p. 1063.