Perceptual modeling in the problem of active object recognition in visual scenes

Pattern Recognition

Volumn 56, Issue , 2016, Pages 129-141

  González Díaz, Iván ^a   Buso, Vincent ^b   Benois Pineau, Jenny ^b  

^a UNIVERSIDAD CARLOS III DE MADRID   (Spain)

^b UNIVERSITÉ DE BORDEAUX   (France)

Author keywords

Active object recognition; Foveal and peripheral pathways; Perceptual modeling; Visual saliency

Indexed keywords

COMPUTER VISION; VISION; VISUALIZATION;

BIOLOGICALLY INSPIRED; COMPUTATIONAL TIME; FOVEAL AND PERIPHERAL PATHWAYS; PERCEPTUAL MODEL; SCENE INTERPRETATION; SLIDING WINDOW METHODS; STATE-OF-THE-ART PERFORMANCE; VISUAL SALIENCY;

OBJECT RECOGNITION;

EID: 84977982500     PISSN: 00313203     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.patcog.2016.03.007     Document Type: Article

References (50)

1. [1] J. Sivic, A. Zisserman, Video Google: a text retrieval approach to object matching in videos, in: Proceedings of the International Conference on Computer Vision, vol. 2, pp. 1470–1477.
2. [2] G. Csurka, C.R. Dance, L. Fan, J. Willamowski, C. Bray, Visual categorization with bags of keypoints, in: Workshop on Statistical Learning in Computer Vision, ECCV, pp. 1–22.
3. [3] P. Viola, M. Jones, Rapid object detection using a boosted cascade of simple features, in: IEEE Conference on Computer Vision and Pattern Recognition, vol. 1, pp. 511–518.
4. [4] N. Dalal, B. Triggs, Histograms of oriented gradients for human detection, in: International Conference on Computer Vision & Pattern Recognition, vol. 2, pp. 886–893.
5. [5] Felzenszwalb, P.F., Girshick, R.B., McAllester, D.A., Ramanan, D., Object detection with discriminatively trained part-based models. IEEE Trans. Pattern Anal. Mach. Intell. 32 (2010), 1627–1645.
6. [6] C.H. Lampert, M.B. Blaschko, T. Hofmann, Beyond sliding windows: object localization by efficient subwindow search, in: IEEE Conference on Computer Vision and Pattern Recognition.
7. [7] Borji, A., Itti, L., State-of-the-art in visual attention modeling. IEEE Trans. Pattern Anal. Mach. Intell. 35 (2013), 185–207.
8. [8] X. Ren, C. Gu, Figure-ground segmentation improves handled object recognition in egocentric video, in: IEEE Conference on Computer Vision and Pattern Recognition.
9. [9] Alexe, B., Deselaers, T., Ferrari, V., Measuring the objectness of image windows. IEEE Trans. Pattern Anal. Mach. Intell. 34 (2012), 2189–2202.
10. [10] Uijlings, J., van de Sande, K., Gevers, T., Smeulders, A., Selective search for object recognition. Int. J. Comput. Vis. 104 (2013), 154–171.
11. [11] Itti, L., Koch, C., Computational modelling of visual attention. Nat. Rev. Neurosci. 2 (2001), 194–203.
12. [12] J. Harel, C. Koch, P. Perona, Graph-based visual saliency, in: Advances in Neural Information Processing Systems, vol. 19, MIT Press, Cambridge, MA, 2007, pp. 545–552.
13. [13] R. de Carvalho Soares, I. da Silva, D. Guliato, Spatial locality weighting of features using saliency map with a BoW approach, in: International Conference on Tools with Artificial Intelligence, 2012, pp. 1070–1075.
14. [14] G. Sharma, F. Jurie, C. Schmid, Discriminative spatial saliency for image classification, in: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2012, pp. 3506–3513.
15. [15] M. San Biagio, L. Bazzani, M. Cristani, V. Murino, Weighted bag of visual words for object recognition, in: IEEE International Conference on Image Processing (ICIP), 2014, pp. 2734–2738.
16. [16] Mahadevan, V., Vasconcelos, N., Biologically inspired object tracking using center-surround saliency mechanisms. IEEE Trans. Pattern Anal. Mach. Intell. 35 (2013), 541–554.
17. [17] Su, Y., Zhao, Q., Zhao, L., Gu, D., Abrupt motion tracking using a visual saliency embedded particle filter. Pattern Recognit. 47 (2014), 1826–1834.
18. [18] E. Vig, M. Dorr, D. Cox, Space-Variant Descriptor Sampling for Action Recognition Based on Saliency and Eye Movements, Springer, Firenze, Italy, pp. 84–97.
19. [19] S. Mathe, C. Sminchisescu, Dynamic eye movement datasets and learnt saliency models for visual action recognition, in: European Conference on Computer Vision (ECCV), 2012, pp. 842–856.
20. [20] I. González-Díaz, V. Buso, J. Benois-Pineau, G. Bourmaud, R. Megret, Modeling instrumental activities of daily living in egocentric vision as sequences of active objects and context for alzheimer disease research, in: ACM MM MIIRH Workshop.
21. [21] Karaman, S., Benois-Pineau, J., Dovgalecs, V., Mégret, R., Pinquier, J., André-Obrecht, R., Gaëstel, Y., Dartigues, J.-F., Hierarchical hidden Markov model in detecting activities of daily living in wearable videos for studies of dementia. Multimed. Tools Appl., 2011, 1–29.
22. [22] A. Fathi, X. Ren, J. M. Rehg, Learning to recognize objects in egocentric activities, in: IEEE Conference on Computer Vision and Pattern Recognition, CVPR, pp. 3281–3288.
23. [23] A. Fathi, Y. Li, J.M. Rehg, Learning to recognize daily actions using gaze, in: European Conference on Computer Vision, ECCV׳ 12, pp. 314–327.
24. [24] K. Ogaki, K. M. Kitani, Y. Sugano, Y. Sato, Coupling eye-motion and ego-motion features for first-person activity recognition, in: IEEE Conference on Computer Vision and Pattern Recognition Workshops, 2012, pp. 1–7.
25. [25] Fernandes, H.L., Stevenson, I.H., Phillips, A.N., Segraves, M.A., Kording, K.P., Saliency and saccade encoding in the frontal eye field during natural scene search. Cereb. Cortex, 2013, 10.1093/cercor/bht179.
26. [26] Wooding, D., Eye movements of large populations: II. Deriving regions of interest, coverage, and similarity using fixation maps. Behav. Res. Methods Instrum. Comput. 34 (2002), 518–528.
27. [27] D. Walther, U. Rutishauser, C. Koch, P. Perona, On the usefulness of attention for object recognition, in: Workshop on Attention and Performance in Computational Vision at ECCV, pp. 96–103.
28. [28] F. Moosmann, D. Larlus, F. Jurie, Learning saliency maps for object categorization, in: ECCV׳06 Workshop on the Representation and Use of Prior Knowledge in Vision.
29. [29] H. Larochelle, G.E. Hinton, Learning to combine foveal glimpses with a third-order Boltzmann machine, in: Advances in Neural Information Processing Systems, vol. 23, pp. 1243–1251.
30. [30] H. Boujut, J. Benois-Pineau, R. Megret, Fusion of multiple visual cues for visual saliency extraction from wearable camera settings with strong motion, in: European Conference on Computer Vision Workshops, 2012.
31. [31] O. Brouard, V. Ricordel, D. Barba, Cartes de Saillance Spatio-Temporelle basées Contrastes de Couleur et Mouvement Relatif, in: Compression et representation des signaux audiovisuels, CORESA 2009, Toulouse, France. 6 p. 2009.
32. [32] C. Chamaret, J.-C. Chevet, O. Le Meur, Spatio-temporal combination of saliency maps and eye-tracking assessment of different strategies, in: IEEE International Conference on Image Processing (ICIP), 2010, pp. 1077–1080.
33. [33] Ramirez-Moreno, D., Schwartz, O., Ramirez-Villegas, J., A saliency-based bottom-up visual attention model for dynamic scenes analysis. Biol. Cybern. 107 (2013), 141–160.
34. [34] Bay, H., Ess, A., Tuytelaars, T., Van Gool, L., Speeded-up robust features (SURF). Comput. Vis. Image Underst. 110 (2008), 346–359.
35. [35] Lowe, D.G., Distinctive image features from scale-invariant keypoints. Int. J. Comput. Vis. 60 (2004), 91–110.
36. [36] V. Sreekanth, A. Vedaldi, C. Jawahar, A. Zisserman, Generalized RBF feature maps for efficient detection, in: British Machine Vision Conference 2010.
37. [37] Wandell, B.A., Foundations of Vision. 1995, Sinauer Associates Inc., Sunderland.
38. [38] S. Liversedge, I. Gilchrist, S. Everling, The Oxford Handbook of Eye Movements, Oxford Library of Psychology, Oxford, 2011 (Chapter 33).
39. [39] Chang, E.-C., Mallat, S., Yap, C., Wavelet foveation. Appl. Comput. Harmon. Anal. 9 (2000), 312–335.
40. [40] J.S. Perry, W. S. Geisler, Gaze-contingent real-time simulation of arbitrary visual fields, in: SPIE Proceedings on Human Vision and Electronic Imaging, pp. 57–69.
41. [41] M. Marszalek, C. Schmid, Spatial weighting for bag-of-features, in: IEEE Conference on Computer Vision & Pattern Recognition, vol. 2, pp. 2118–2125.
42. [42] J. Yang, K. Yu, Y. Gong, T. Huang, Linear spatial pyramid matching using sparse coding for image classification, in: IEEE Conference on Computer Vision and Pattern Recognition, 2009, pp. 1794–1801.
43. [43] J. Wang, J. Yang, K. Yu, F. Lv, T. Huang, Y. Gong, Locality-constrained linear coding for image classification, in: IEEE Conference on Computer Vision and Pattern Recognition.
44. [44] Wei, C.-P., Chao, Y.-W., Yeh, Y.-R., Wang, Y.-C.F., Locality-sensitive dictionary learning for sparse representation based classification. Pattern Recognit. 46 (2013), 1277–1287.
45. [45] H. Pirsiavash, D. Ramanan, Detecting activities of daily living in first-person camera views, in: 2012 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).
46. [46] B. Yao, L. Fei-Fei, Grouplet: a structured image representation for recognizing human and object interactions, in: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), San Francisco, USA.
47. [47] Everingham, M., Gool, L., Williams, C.K., Winn, J., Zisserman, A., The pascal visual object classes (VOC) challenge. Int. J. Comput. Vis. 88 (2010), 303–338.
48. [48] J. Deng, W. Dong, R. Socher, L.-J. Li, K. Li, L. Fei-Fei, ImageNet: a large-scale hierarchical image database, in: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2009.
49. [49] S. Lazebnik, C. Schmid, J. Ponce, Beyond bags of features: spatial pyramid matching for recognizing natural scene categories, in: IEEE Conference on Computer Vision and Pattern Recognition, 2006, pp. 2169–2178.
50. [50] B. Yao, A. Khosla, L. Fei-Fei, Combining randomization and discrimination for fine-grained image categorization, in: IEEE Conference on Computer Vision and Pattern Recognition, 2011.