Object recognition with hierarchical discriminant saliency networks

Frontiers in Computational Neuroscience

Volumn 8, Issue , 2014, Pages

  Han, Sunhyoung ^a   Vasconcelos, Nuno ^b  

^a Analytics Department ID Analytics   (United States)

^b UNIVERSITY OF CALIFORNIA   (United States)

Author keywords

Discriminant saliency; Hierarchical network; Object detection; Object recognition; Top down saliency

Indexed keywords

CONVOLUTION; NEURAL NETWORKS; OBJECT DETECTION; VISUALIZATION;

CONVOLUTIONAL NEURAL NETWORK; DISCRIMINANT SALIENCY; FUNCTIONAL ENHANCEMENTS; HIERARCHICAL NETWORK; NEUROPHYSIOLOGICAL MODEL; STATISTICAL INTERPRETATION; STATISTICAL LEARNING; TOPDOWN;

OBJECT RECOGNITION;

ARTICLE; ATTENTION; CONTROLLED STUDY; HIERARCHICAL DISCRIMINANT SALIENCY NETWORK; INTERMETHOD COMPARISON; MATHEMATICAL COMPUTING; MATHEMATICAL MODEL; NERVE CELL NETWORK; OBJECT RECOGNITION; PERCEPTION; VISUAL CORTEX; VISUAL DISCRIMINATION; VISUAL ORIENTATION;

EID: 84907158054     PISSN: None     EISSN: 16625188     Source Type: Journal    
DOI: 10.3389/fncom.2014.00109     Document Type: Article

References (94)

1. Boiman, O., Shechtman, E., and Irani, M. (2008). "In defense of nearest-neighbor based image classification," inIEEE Conference in Computer Vision and Pattern Recognition (Anchorage, AK).
2. Borji, A., and Itti, L. (2013). State-of-the-art in visual attention modeling. IEEE Trans. Patt. Anal. Mach. Intell. 35, 185-207. doi: 10.1109/TPAMI.2012.89
3. Borji, A., Sihite, D., and Itti, L. (2013). Quantitative analysis of human-model agreement in visual saliency modeling: a comparative study. IEEE Trans. Image Process. 22, 55-69. doi: 10.1109/TIP.2012.2210727
4. Brincat, S., and Connor, C. (2004). Underlying principles of visual shape selectivity in posterior inferotemporal cortex. Nat. Neurosci. 7, 880-886. doi: 10.1038/nn1278
5. Bruce, N., and Tsotsos, J. (2006). "Saliency based on information maximization," in Neural Information Processing Systems (Vancouver, BC: MIT Press).
6. Buccigrossi, R., and Simoncelli, E. (1999). Image compression via joint statistical characterization in the wavelet domain. IEEE Trans. Image Process. 8, 1688-1701. doi: 10.1109/83.806616
7. Carandini, M., Demb, J., Mante, V., Tolhurst, D., Dan, Y., Olshausen, B., et al. (2005). Do we know what the early visual system does? J. Neurosci. 25, 10577-10597. doi: 10.1523/JNEUROSCI.3726-05.2005
8. Carandini, M., and Heeger, D. (2011). Normalization as a canonical neural computation. Nat. Rev. Neurosci. 13, 51-62. doi: 10.1038/nrn3136
9. Carandini, M., Heeger, D. J., and Movshon, J. A. (1997). Linearity and normalization in simple cells of the macaque primary visual cortex. J. Neurosci. 17, 8621-8644.
10. Carneiro, G., Chan, A., Moreno, P., and Vasconcelos, N. (2007). Supervised learning of semantic classes for image annotation and retrieval. IEEE Trans. Patt. Anal. Mach. Intell. 29, 394-410. doi: 10.1109/TPAMI.2007.61
11. Chang, S., Bin, Y., and Vetterli, M. (2000). Adaptive wavelet thresholding for image denoising and compression.IEEE Trans. Image Process. 9, 1532-1546. doi: 10.1109/83.862633
12. Chatfield, K., Lempitsky, V., Vedaldi, A., and Zisserman, A. (2011). "The devil is in the details: an evaluation of recent feature encoding methods," in British Machine Vision Conference (Dundee).
13. Coates, A., Ng, A. Y., and Lee, H. (2011). "An analysis of single-layer networks in unsupervised feature learning," inInternational Conference on Artificial Intelligence and Statistics (Fort Lauderdale, FL), 215-223.
14. Csurka, G., Dance, C., Fan, L., Williamowski, J., and Bray, C. (2004). "Visual categorization with bags of keypoints," in In ECCV Workshop on Statistical Learning in Computer Vision (Prague).
15. Dalal, N., and Triggs, B. (2005). "Histograms of oriented gradients for human detection," in IEEE Conference in Computer Vision and Pattern Recognition (San Diego, CA).
16. Desimone, R., and Duncan, J. (1995). Neural mechanisms of selective visual attention. Ann. Rev. Neurosci. 18, 193-222. doi: 10.1146/annurev.ne.18.030195.001205
17. Dixit, M., Rasiwasia, N., and Vasconcelos, N. (2011). "Adapted gaussian models for image classification," in IEEE Conference in Computer Vision and Pattern Recognition (Colorado Springs, CO).
18. Do, M., and Vetterli, M. (2002). Wavelet-based texture retrieval using generalized gaussian density and kullback-leibler distance. IEEE Trans. Image Process. 11, 146-158. doi: 10.1109/83.982822
19. Dorko, G., and Schmid, C. (2005). Object Class Recognition using Discriminative Local Features. Technical Report RR-5497, INRIA - Rhone-Alpes, February 2005.
20. Duda, R., Hart, P., and Stork, D. (2001). Pattern Classification. New York, NY: John Wiley & Sons.
21. Elazary, L., and Itti, L. (2010). A bayesian model for efficient visual search and recognition. Vision Res. 50, 1338-1352. doi: 10.1016/j.visres.2010.01.002
22. Ezzat, T., and Poggio, T. (2000). Visual speech synthesis by morphing visemes. Int. J. Comput. Vis. 38, 45-57. doi: 10.1023/A:1008166717597
23. Fei-Fei, L., Fergus, R., and Perona, P. (2005). "Learning generative visual models from few training examples: an incremental bayesian approach tested on 101 object categories," in CVPR Workshop on Generative Model-Based Vision (San Diego, CA).
24. Fei-Fei, L., and Perona, P. (2005). "A bayesian hierarchical model for learning natural scene categories," in IEEE Conference in Computer Vision and Pattern Recognition (San Diego, CA).
25. Felzenszwalb, P., Girshick, R., McAllester, D., and Ramanan, D. (2009). Object detection with discriminatively trained part based models. IEEE Trans. Patt. Anal. Mach. Intell. 32, 1627-1645. doi: 10.1109/TPAMI.2009.167
26. Fukushima, K. (1980). Neocognition: a self-organizing neural network model for a mechanism of pattern recognition unaffected by shift in position. Biol. Cybern. 36, 193-202. doi: 10.1007/BF00344251
27. Gao, D., Han, S., and Vasconcelos, N. (2009). Discriminant saliency, the detection of suspicious coincidences, and applications to visual recognition. IEEE Trans. Patt. Anal. Mach. Intell. 31, 989-1005. doi: 10.1109/TPAMI.2009.27
28. Gao, D., Mahadevan, V., and Vasconcelos, N. (2008). On the plausibility of the discriminant center-surround hypothesis for visual saliency. J. Vis. 8, 1-18. doi: 10.1167/8.7.13
29. Gao, D., and Vasconcelos, N. (2005). "Integrated learning of saliency, complex features, and object detectors from cluttered scenes," in IEEE Conference in Computer Vision and Pattern Recognition (San Diego, CA).
30. Gao, D., and Vasconcelos, N. (2009). Decision-theoretic saliency: computational principles, biological plausibility, and implications for neurophysiology and psychophysics. Neural Comput. 21, 239-271. doi: 10.1162/neco.2009.11-06-391
31. Geman, S., and Geman, D. (1984). Stochastic relaxation, gibbs distributions, and the bayesian restoration of images.Patt. Anal. Mach. Intell. IEEE Trans. 6, 721-741. doi: 10.1109/TPAMI.1984.4767596
32. Geusebroek, J., Burghouts, G., and Smeulders, A. (2005). The amsterdam library of object images. Int. J. Comput. Vis. 61, 103-112. doi: 10.1023/B:VISI.0000042993.50813.60
33. Graham, N. (2011). Beyond multiple pattern analyzers modeled as linear filters (as classical v1 simple cells): useful additions of the last 25 years. Vision Res. 51, 1397-1430. doi: 10.1016/j.visres.2011.02.007
34. Guo, C., Ma, Q., and Zhang, L. (2008). "Spatio-temporal saliency detection using phase spectrum of quaternion fourier transform," in IEEE Conference in Computer Vision and Pattern Recognition (Anchorage, AK).
35. Han, S., and Vasconcelos, N. (2010). Biologically plausible saliency mechanisms improve feedforward object recognition. Vision Res. 50, 2295-2307. doi: 10.1016/j.visres.2010.05.034
36. Han, S., and Vasconcelos, N. (2011). "Biologically Plausible Detection of Amorphous Objects in the Wild," in 2011 IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops (CVPRW) (Colorado Springs, CO).
37. He, X., Zemel, R., and Carreira-Perpinan, M. (2004). "Multiscale conditional random fields for image labeling," inIEEE Conference in Computer Vision and Pattern Recognition (Washington, DC).
38. Heeger, D. (1992). Normalization of cell responses in cat striate cortex. Vis. Neurosci. 9, 181-197. doi: 10.1017/S0952523800009640
39. Hinton, G. E., Osindero, S., and Teh, Y.-W. (2006). A fast learning algorithm for deep belief nets. Neural Comput. 18, 1527-1554. doi: 10.1162/neco.2006.18.7.1527
40. Hou, X., and Zhang, L. (2007). "Saliency detection: a spectral residual approach," in IEEE Conference in Computer Vision and Pattern Recognition (Minneapolis, MN).
41. Huang, J., and Mumford, D. (1999). "Statistics of natural images and models," in IEEE Conference in Computer Vision and Pattern Recognition (Fort Collins, CO).
42. Hubel, D. H., and Wiesel, T. N. (1962). Receptive field, binocular interaction, and functional architecture of the cat's visual cortex. J. Physiol. 160, 106.
43. Itti, L., Koch, C., and Niebur, E. (1998). A model of saliency-based visual attention for rapid scene analysis. IEEE Trans. Patt. Anal. Mach. Intell. 20, 1254-1259. doi: 10.1109/34.730558
44. Jarrett, K., Kavukcuoglu, K., Ranzato, M., and LeCun, Y. (2009). "What is the best multi-stage architecture for object recognition?," in IEEE Conference in Computer Vision and Pattern Recognition (Miami Beach, FL).
45. Kadir, T., and Brady, M. (2001). Scale, saliency and image description. Int. J. Comput. Vis. 45, 83-105. doi: 10.1023/A:1012460413855
46. Kanan, C., and Cottrell, G. (2010). "Robust classification of objects, faces, and flowers using natural image statistics," in IEEE Conference in Computer Vision and Pattern Recognition (San Francisco, CA).
47. Kavukcuoglu, K., Sermanet, P., Boureau, Y.-L., Gregor, K., Mathieu, M., and LeCun, Y. (2010). "Learning convolutional feature hierarchies for visual recognition," in Neural Information Processing Systems (Vancouver, BC).
48. Krizhevsky, A., Sutskever, I., and Hinton, G. (2012). "ImageNet classification with deep convolutional neural networks," in Neural Information Processing Systems (Lake Tahoe, NV).
49. Kulis, B., Saenko, K., and Darrell, T. (2011). "What you saw is not what you get: domain adaptation using asymmetric kernel transforms," in IEEE Conference in Computer Vision and Pattern Recognition (Colorado Springs, CO), 1785-1792.
50. Lazebnik, S., Schmid, C., and Ponce, J. (2006). "Beyond bags of features: spatial pyramid matching for recognizing natural scene categories," in IEEE Conference in Computer Vision and Pattern Recognition (New York, NY).
51. LeCun, Y., Boser, B., Denker, J., Henderson, D., Howard, R., Hubbard, W., et al. (1990). "Handwritten digit recognition with a back-propagation network," in Neural Information Processing Systems (Denver, CO: MIT Press).
52. LeCun, Y., Bottou, L., Bengio, Y., and Haffiner, P. (1998). Gradient-based learning applied to document recognition.Proc. IEEE 86, 2278-2324. doi: 10.1109/5.726791
53. Leung, T., and Malik, J. (2001). Representing and recognizing the visual appearance of materials using three-dimensional textons. Int. J. Comput. Vis. 43, 29-44. doi: 10.1023/A:1011126920638
54. Lowe, D. G. (1999). "Object recognition from local scale-invariant features," in International Conference on Computer Vision (Corfu).
55. Mahadevan, V., and Vasconcelos, N. (2013). Biologically-inspired object tracking using center-surround mechanisms. IEEE Trans. Patt. Anal. Mach. Intell. 35, 541-554. doi: 10.1109/TPAMI.2012.98
56. Mairal, J., Bach, F., Ponce, J., Sapiro, G., and Zisserman, A. (2008). "Discriminative learned dictionaries for local image analysis," in IEEE Conference in Computer Vision and Pattern Recognition (Anchorage, AK).
57. Mallat, S., and Zhang, Z. (1993). Matching pursuits with time-frequency dictionaries. IEEE Trans. Signal Process. 41, 3397-3415. doi: 10.1109/78.258082
58. Maunsell, J., and Treue, S. (2006). Feature-based attention in visual cortex. Trends Nerosci. 29, 317-322. doi: 10.1016/j.tins.2006.04.001
59. Miau, K., and Schmid, C. (2001). "Neuromorphic algorithms for computer vision and attention," in Proceedings of SPIE 46 Annual International Symposium on Optical Science and Technology (San Diego, CA).
60. Moosmann, F., Triggs, B., and Jurie, F. (2007). "Randomized clustering forests for building fast and discriminative visual vocabularies," in Neural Information Processing Systems (Vancouver, BC: MIT Press).
61. Mutch, J., and Lowe, D. (2008). Object class recognition and localization using sparse features with limited receptive fields. Int. J. Comput. Vis. 80, 45-57. doi: 10.1007/s11263-007-0118-0
62. Nair, V., and Hinton, G. E. (2010). "Rectified linear units improve restricted boltzmann machines," in International Conference on Machine Learning (Haifa), 807-814.
63. Navalpakkam, V., and Itti, L. (2007). Search goal tunes visual features optimally. Neuron 53, 605-617. doi: 10.1016/j.neuron.2007.01.018
64. Perrett, D., and Oram, M. (1993). Neurophysiology of shape processing. Imaging Vis. Comput. 11, 317-333. doi: 10.1016/0262-8856(93)90011-5
65. Pinto, N., Cox, D., and DiCarlo, J. (2008). Why is real-world visual object recognition hard? PLoS Comput. Biol. 4:e27. doi: 10.1371/journal.pcbi.0040027
66. Pinto, N., Doukhan, D., DiCarlo, J., and Cox, D. (2009). A high-throughput screening approach to discovering good forms of biologically inspired visual representation. PLoS Comput. Biol. 5:e1000579. doi: 10.1371/journal.pcbi.1000579
67. Poggio, T., and Edelman, S. (1990). A network that learns to recognize 3d objects. Nature 343, 263-266. doi: 10.1038/343263a0
68. Riesenhuber, M., and Poggio, T. (1999). Hierarchical models of object recognition in cortex. Nat. Neurosci. 2, 1019-1025. doi: 10.1038/14819
69. Rosenholtz, R. (1999). A simple saliency model predicts a number of motion popout phenomena. Vision Res. 39, 3157-3163. doi: 10.1016/S0042-6989(99)00077-2
70. Saenko, K., Kulis, B., Fritz, M., and Darrell, T. (2010). "Adapting visual category models to new domains," inEuropean Conference in Computer Vision (Creta), 213-226.
71. Sebe, N., and Lew, M. S. (2003). Comparing salient point detectors. Patt. Recogn. Lett. 24, 89-96. doi: 10.1016/S0167-8655(02)00192-7
72. Serre, T., Wolf, L., Bileschi, S., Riesenhuber, M., and Poggio, T. (2007). Robust object recognition with cortex-like mechanisms. IEEE Trans. Patt. Anal. Mach. Intell. 29, 411-426. doi: 10.1109/TPAMI.2007.56
73. Sivic, J., and Zisserman, A. (2003). "Video google: a text retrieval approach to object matching in videos," inInternational Conference on Computer Vision (Nice).
74. Srivastava, A., Lee, A., Simoncelli, E., and Zhu, S. (2003). On advances in statistical modeling of natural images.Math. Imaging Vis. 18, 17-33. doi: 10.1023/A:1021889010444
75. Treisman, A. (1985). Preattentive processing in vision. Comput. vis. Graph. Image Process. 31, 156-177. doi: 10.1016/S0734-189X(85)80004-9
76. Tsotsos, J. (1991). Computational resources do constrain behavior. Behav. Brain Sci. 14, 506-507. doi: 10.1017/S0140525X00071053
77. Vasconcelos, N. (2004a). Minimum probability of error image retrieval. IEEE Trans. Signal Process. 52, 2322-2336. doi: 10.1109/TSP.2004.831125
78. Vasconcelos, N. (2004b). On the efficient evaluation of probabilistic similarity functions for image retrieval. IEEE Trans. Inform. Theory 50, 1482-1496. doi: 10.1109/TIT.2004.830760
79. Vasconcelos, N., and Lippman, A. (1997). "Library-based coding: a representation for efficient video compression and retrieval," in Data Compression Conference (Snowbird, UT).
80. Vasconcelos, N., and Lippman, A. (2000). "A probabilistic architecture for content-based image retrieval," in IEEE Conference in Computer Vision and Pattern Recognition (Hilton Head Island, SC).
81. Vidal-Naquet, M., and Ullman, S. (2003). "Object recognition with informative features and linear classification," inIEEE Conference in Computer Vision and Pattern Recognition (Madison, WI).
82. Viola, P., and Jones, M. (2004). Robust real-time face detection. Int. J. Comput. Vis. 57, 137-154. doi: 10.1023/B:VISI.0000013087.49260.fb
83. Walther, D., and Koch, C. (2006). Modeling attention to salient proto-objects. Neural Netw. 19, 1395-1407. doi: 10.1016/j.neunet.2006.10.001
84. Wang, J., Yang, J., Yu, K., Lv, F., Huang, T., and Gong, Y. (2010). "Locality-constrained linear coding for image classification," in IEEE Conference on Computer Vision and Pattern Recognition (San Francisco, CA), 3360-3367.
85. Winn, J., Criminisi, A., and Minka, T. (2005). "Object categorization by learned universal visual dictionary," inInternational Conference on Computer Vision (Beijing).
86. Wolfe, J. M. (1994). Guided search 2.0: a revised model of visual search. Psychon. Bull. Rev. 1, 202-238. doi: 10.3758/BF03200774
87. Wolfe, J. M. (1998). Visual memory: what do you know about what you saw? Curr. Biol. 8, R303-R304. doi: 10.1016/S0960-9822(98)70192-7
88. Yamane, Y., Carlson, E., Bowman, K., Wang, Z., and Connor, C. (2008). A neural code for three-dimensional object shape in macaque inferotemporal cortex. Nat. Neurosci. 11, 1352-1360. doi: 10.1038/nn.2202
89. Yang, J., Yu, K., and Gong, Y. (2009). "Linear spatial pyramid matching using sparse coding for image classification," in IEEE Conference in Computer Vision and Pattern Recognition (Miami Beach, FL).
90. Yarbus, A. (1967). Eye Movements and Vision. New York, NY: Plenum. doi: 10.1007/978-1-4899-5379-7
91. Zhang, H., Berg, A., Maire, M., and Malik, J. (2006). "SVM-KNN: discriminative nearest neighbor classification for visual category recognition," in IEEE Conference on Computer Vision and Pattern Recognition (New York, NY).
92. Zhang, J., Marszalek, M., Lazebnik, S., and Schmid, C. (2007). Local features and kernels for classification of texture and object categories: a comprehensive study. Int. J. Comput. Vis. 73, 213-238. doi: 10.1007/s11263-006-9794-4
93. Zhang, L., Tong, M., Marks, H., Tim, K., Shan, H., and Cottrell, G. (2008). Sun: a bayesian framework for saliency using natural statistics. J. Vis. 8:32. doi: 10.1167/8.7.32
94. Zhou, X., Cui, N., Li, Z., Liang, F., and Huang, T. (2009). "Hierarchical gaussianization for image classification," inIEEE Conference in Computer Vision and Pattern Recognition (Miami Beach, FL).