A framework for visual-context-aware object detection in still images

Computer Vision and Image Understanding

Volumn 114, Issue 6, 2010, Pages 700-711

  Perko, Roland ^a   Leonardis, Aleš ^a  

^a UNIVERSITY OF LJUBLJANA   (Slovenia)

Author keywords

Context integration; Object detection; Visual context

Indexed keywords

COMPUTER VISION; INTEGRATION; NETWORK FUNCTION VIRTUALIZATION; OBJECT RECOGNITION;

BOTTOM-UP SALIENCIES; CONDITIONAL PROBABILITIES; CONTEXTUAL FEATURE; CONTEXTUAL INFORMATION; KERNEL DENSITY ESTIMATION; PROBABILISTIC FRAMEWORK; STATE-OF-THE-ART METHODS; VISUAL CONTEXT;

OBJECT DETECTION;

EID: 78651389910     PISSN: 10773142     EISSN: 1090235X     Source Type: Journal    
DOI: 10.1016/j.cviu.2010.03.005     Document Type: Article

References (53)

1. E. Aminoff, N. Gronau, M. Bar, The parahippocampal cortex mediates spatial and nonspatial associations, Cereb. Cortex 17 (7) (2007) 1493-1503.
2. M. Bar, Visual objects in context, Nat. Rev. Neurosci. 5 (2004) 617-629. (Pubitemid 39013593)
3. I. Biederman, Perceiving real-world scenes, Science 177 (4043) (1972) 77-80.
4. I. Biederman, On the Semantics of a Glance at a Scene, Perceptual Organization, Lawrence Erlbaum, 1981 (Chapter 8, pp. 213-263).
5. S.M. Bileschi, StreetScenes: Towards Scene Understanding in Still Images. PhD Thesis, Massachusetts Institute of Technology, May 2006.
6. C. Carson, S. Belongie, H. Greenspan, J. Malik, Blobworld: Image segmentation using expectation-maximization and its application to image querying, IEEE Trans. Pattern Anal. Mach. Intell. 24 (8) (2002) 1026-1038.
7. N. Dalal, B. Triggs, Histograms of oriented gradients for human detection, in: Proc. Conf. Comp. Vis. Pattern Recog., vol. 2, 2005, pp. 886-893. (Pubitemid 43897286)
8. S.K. Divvala, D. Hoiem, J.H. Hays, A.A. Efros, M. Hebert, An empirical study of context in object detection, in: Proc. Conf. Comp. Vis. Pattern Recog., June 2009.
9. M. Everingham, A. Zisserman, C.K.I. Williams, L. Van Gool, The PASCAL Visual Object Classes Challenge 2006 (VOC2006) Results, September 2006. .
10. A. Friedman, Framing pictures: The role of knowledge in automatized encoding and memory for gist, J. Exp. Psychol. Gen. 108 (8) (1979) 316-355.
11. M. Girolami, C. He, Probability density estimation from optimally condensed data samples, IEEE Trans. Pattern Anal. Mach. Intell. 25 (October) (2003) 1253- 1264.
12. J.O.S. Goh, S.C. Siong, D. Park, A. Gutchess, A. Hebrank, M.W.L. Chee, Cortical areas involved in object, background, and object-background processing revealed with functional magnetic resonance adaptation, J. Neurosci. 24 (45) (2004) 10223-10228. (Pubitemid 39492049)
13. J. Harel, C. Koch, On the optimality of spatial attention for object detection, in: Int. Works. Attention in Cognit. Sys., vol. 5, May 2008, pp. 27-40.
14. C. Harris, M. Stephens, A combined corner and edge detector, in: Proc. 4th Alvey Visual Conf., 1988, pp. 189-192.
15. D. Heesch, M. Petrou, Non-Gibbsian Markov random field models for contextual labelling of structured scenes, in: Proc. British Mach. Vis. Conf., September 2007.
16. J.M. Henderson, A. Pollatsek, K. Rayner, The effects of foveal priming and extrafoveal preview on object identification, J. Exp. Psychol. Hum. Percept. Perform. 13 (3) (1987) 449-463.
17. D. Hoiem, A.A. Efros, M. Hebert, Geometric context from a single image, in: Proc. Int. Conf. Comp. Vis., vol. 1, October 2005, pp. 654-661. (Pubitemid 44055018)
18. D. Hoiem, A.A. Efros, M. Hebert, Putting objects in perspective, in: Proc. Conf. Comp. Vis. Pattern Recog., vol. 2, June 2006, pp. 2137-2144. (Pubitemid 44931578)
19. D. Hoiem, A.A. Efros, M. Hebert, Closing the loop on scene interpretation, in: Proc. Conf. Comp. Vis. Pattern Recog., June 2008.
20. L. Itti, C. Koch, Computational modeling of visual attention, Nat. Rev. Neurosci. 2 (3) (2001) 194-203.
21. T. Joachims, Making large-scale support vector machine learning practical, in: Advances in Kernel Methods: Support Vector Machines, MIT Press, Cambridge, MA, 1999, pp. 41-56 (Chapter 11).
22. M.C. Jones, J.S. Marron, S.J. Sheather, A brief survey of bandwidth selection for density estimation, J. Am. Stat. Assoc. 91 (433) (1996) 401-407. (Pubitemid 126353404)
23. M. Kristan, D. Skoçaj, A. Leonardis, Incremental learning with Gaussian mixture models, in: Comp. Vis. Winter Works., February 2008, pp. 25-32.
24. H. Kruppa, B. Schiele, Using local context to improve face detection, in: Proc. British Mach. Vis. Conf., September 2003.
25. B. Leibe, Interleaved Object Categorization and Segmentation, PhD Thesis, ETH Zurich, PhD Thesis No. 15752, October 2004.
26. B. Leibe, A. Leonardis, B. Schiele, Robust object detection with interleaved categorization and segmentation, Int. J. Comput. Vision 77 (1-3) (2008) 259- 289.
27. T. Malisiewicz, A.A. Efros, Beyond categories: The visual memex model for reasoning about object relationships, in: Neural Inf. Proc. Systems, December 2009.
28. A. Oliva, A. Torralba, Modeling the shape of the scene: A holistic representation of the spatial envelope, Int. J. Comput. Vision 42 (3) (2001) 145-175. (Pubitemid 32680801)
29. A. Oliva, A. Torralba, The role of context in object recognition, Trends Cogn. Sci. 11 (12) (2007) 520-527. (Pubitemid 350181571)
30. D. Omerçević, O. Drbohlav, A. Leonardis, High-dimensional feature matching: Employing the concept of meaningful nearest neighbors, in: Proc. Int. Conf. Comp. Vis., October 2007.
31. S.E. Palmer, The effects of contextual scenes on the identification of objects, Mem. Cogn. 3 (1975) 519-526.
32. C. Papageorgiou, T. Poggio, A trainable system for object detection, Int. J. Comput. Vision 38 (1) (2000) 15-33.
33. J. Pearl, Probabilistic Reasoning in Intelligent Systems: Networks of Plausible Inference, Morgan Kaufmann Publishers, 1988.
34. R. Perko, A. Leonardis, Context driven focus of attention for object detection, in: Int. Works. Attention in Cognit. Sys., vol. 4840, December 2007, pp. 216- 233 (Chapter 14). (Pubitemid 351153884)
35. R. Perko, C. Wojek, B. Schiele, A. Leonardis, Probabilistic combination of visual context based attention and object detection, in: Int. Works. Attention in Cognit. Sys., vol. 5, May 2008, pp. 166-179.
36. J.C. Platt, Probabilistic outputs for support vector machines and comparisons to regularized likelihood methods, Adv. Large Margin Classifiers 10 (3) (1999) 61-74.
37. A. Quattoni, A. Torralba, Recognizing indoor scenes, in: Proc. Conf. Comp. Vis. Pattern Recog., June 2009.
38. X. Ren, C.C. Fowlkes, J. Malik, Figure/ground assignment in natural images, in: Proc. European Conf. Comp. Vis., vol. 2, May 2006, pp. 614-627. (Pubitemid 44029759)
39. B.C. Russell, A. Torralba, K.P. Murphy, W.T. Freeman, LabelMe: A database and web-based tool for image annotation, Technical Report AIM-2005-025, MIT AI Lab Memo, September 2005.
40. P. Sabzmeydani, G. Mori, Detecting pedestrians by learning shapelet features, in: Proc. Conf. Comp. Vis. Pattern Recog., June 2007.
41. B. Schiele, J.L. Crowley, Recognition without correspondence using multidimensional receptive field histograms, Int. J. Comput. Vision 36 (1) (2000) 31-50. (Pubitemid 30573776)
42. E. Seemann, B. Leibe, B. Schiele, Multi-aspect detection of articulated objects, in: Proc. Conf. Comp. Vis. Pattern Recog., vol. 2, June 2006, pp. 1582-1588. (Pubitemid 44931508)
43. T. Serre, L. Wolf, S. Bileschi, M. Riesenhuber, T. Poggio, Robust object recognition with cortex-like mechanisms, IEEE Trans. Pattern Anal. Mach. Intell. 29 (3) (2007) 411-426. (Pubitemid 46336417)
44. A. Torralba, Contextual modulation of target saliency, in: Neural Inf. Proc. Systems, vol. 14, 2002, pp. 1303-1310.
45. A. Torralba, Contextual priming for object detection, Int. J. Comput. Vision 53 (2) (2003) 153-167.
46. A. Torralba, A. Oliva, M.S. Castelhano, J.M. Henderson, Contextual guidance of attention in natural scenes: The role of global features on object search, Psychol. Rev. 113 (4) (2006) 766-786. (Pubitemid 44629917)
47. A. Torralba, P. Sinha, Statistical context priming for object detection, in: Proc. Int. Conf. Comp. Vis., vol. 1, July 2001, pp. 763-770. (Pubitemid 32795031)
48. P. Viola, M. Jones, Rapid object detection using a boosted cascade of simple features, in: Proc. Conf. Comp. Vis. Pattern Recog., December 2001.
49. D. Walther, C. Koch, Modeling attention to salient proto-objects, Neural Networks 19 (9) (2006) 1395-1407. (Pubitemid 44708603)
50. M.P. Wand, M.C. Jones, Kernel Smoothing, Chapman & Hall/CRC, 1995.
51. C. Wojek, G. Dorkó, A. Schulz, B. Schiele, Sliding-windows for rapid object class localization: A parallel technique, in: DAGM-Symposium, vol. 30, June 2008, pp. 71-81.
52. C. Wojek, B. Schiele, A performance evaluation of single and multi-feature people detection, in: DAGM-Symposium, vol. 30, June 2008, pp. 82-91.
53. L. Wolf, S.M. Bileschi, A critical view of context, Int. J. Comput. Vision 69 (2) (2006) 251-261. (Pubitemid 43814120)