Renewing the respect for similarity

Frontiers in Computational Neuroscience

Volumn 6, Issue JUL, 2012, Pages

  Edelman, Shimon ^a   Shahbazi, Reza ^a  

^a Cornell University ^*  (United States)

Author keywords

Object recognition; Scene interpretation; Scene space; Shape space; Similarity; View space; Visual structure

Indexed keywords

ASSOCIATIVE PROCESSING; COMPUTER VISION; INFORMATION MANAGEMENT; OBJECT RECOGNITION; SOFTWARE TESTING;

SCENE INTERPRETATION; SCENE SPACE; SHAPE SPACE; SIMILARITY; VIEW SPACE; VISUAL STRUCTURE;

COMPUTATION THEORY;

ALGORITHM; ARTICLE; ASSOCIATIVE MEMORY; COGNITION; DEPTH PERCEPTION; HUMAN; INFORMATION RETRIEVAL; LEARNING; LOCALITY SENSITIVE HASHING; MATHEMATICAL ANALYSIS; MEMORY; MENTAL FUNCTION; NONHUMAN; RECOGNITION; SIMILARITY; STATISTICS; VISUAL SYSTEM;

EID: 84866045912     PISSN: 16625188     EISSN: 16625188     Source Type: Journal    
DOI: 10.3389/fncom.2012.00045     Document Type: Article

References (148)

1. Adriaans, P., and Vitányi, P. M. B. (2007). “The power and perils of MDL,” in Proceedings of the IEEE International Symposium on Information Theory (ISIT) (Nice, France), 2216-2220.
2. Aho, A. V., Hopcroft, J. E., and Ullman, J. D. (1974). The Design and Analysis of Computer Algorithms. Reading, MA: Addison-Wesley.
3. Andoni, A., and Indyk, P. (2008). Near-optimal hashing algorithms for approximate nearest neighbor in high dimensions. Commun. ACM 51, 117-122.
4. Attneave, F. (1950). Dimensions of similarity. Am. J. Psychol. 63, 516-556.
5. Bar, M. (2004). Visual objects in context. Nat. Rev. Neurosci. 5, 617-629.
6. Bar, M. (ed.). (2011). Prediction in the Brain. New York, NY: Oxford University Press.
7. Bartal, Y., Recht, B., and Schulman, L. J. (2011). “Dimensionality reduction: beyond the Johnson-Lindenstrauss bound,” in Proceedings of the 22nd SODA (ACM-SIAM Symposium on Discrete Algorithms), 86
8. Basile, P., Caputo, A., and Semeraro, G. (2011). “Encoding syntactic dependencies by vector permutation,” in Proceedings of the GEMS 2011 Workshop on Geometrical Models of Natural Language Semantics, EMNLP 2011, eds S. Padó and Y. Peirsman (Edinburgh, Scotland), 4351.
9. Beals, R., Krantz, D. H., and Tversky, A. (1968). The foundations of multidimensional scaling. Psychol. Rev. 75, 127-142.
10. Bellman, R. E. (1961). Adaptive Control Processes. Princeton, NJ: Princeton University Press.
11. Bienenstock, E., Geman, S., and Potter, D. (1997). “Compositionality, MDL priors, and object recognition,” in Neural Information Processing Systems, Vol. 9, eds M. C. Mozer, M. I. Jordan, and T. Petsche (Cambridge, MA: MIT Press), 838-844.
12. Bishop, C. M. (2006). Pattern Recognition and Machine Learning. Berlin: Springer.
13. Bongard, M. M. (1970). Pattern Recognition. Rochelle Park, NJ: Spartan Books.
14. Bourgain, J. (1985). On Lipschitz embedding of finite metric spaces in Hilbert space. Israel J. Math. 52, 46-52.
15. Brady, T. F., Konkle, T., Alvarez, G. A., and Oliva, A. (2008). Visual long-term memory has a massive storage capacity for object details. Proc. Natl. Acad. Sci. U.S.A. 105, 14325-14329.
16. Broder, A. Z. (1997). “On the resemblance and containment of documents,” in Proceedings of the Compression and Complexity of Sequences, (Salerno, Italy), 21-27.
17. Bülthoff, H. H., and Edelman, S. (1992). Psychophysical support for a 2-D view interpolation theory of object recognition. Proc. Natl. Acad. Sci. U.S.A. 89, 60-64.
18. Chater, N., and Vitányi, P. (2003). The generalized universal law of generalization. J. Math. Psychol. 47, 346-369.
19. Chater, N., Tenenbaum, J. B., and Yuille, A. (2006). Probabilistic models of cognition: conceptual foundations. Trends Cogn. Sci. 10, 287-291.
20. Connor, C. E., Preddie, D. C., Gallant, J. L., and Van Essen, D. C. (1997). Spatial attention effects in macaque area V4. J. Neurosci. 17, 3201-3214.
21. Cortes, C., and Vapnik, V. (1995). Support-vector networks. Mach. Learn. 20, 273-297.
22. Cover, T., and Hart, P. (1967). Nearest neighbor pattern classification. IEEE Trans. Inf. Theory 13, 21-27.
23. Cox, D. D., Meier, P., Oertelt, N., and DiCarlo, J. J. (2005). ‘Breaking’ position-invariant object recognition. Nat. Neurosci. 8, 1145-1147.
24. Craik, K. J. W. (1943). The Nature of Explanation. Cambridge, England: Cambridge University Press.
25. Cutzu, F., and Edelman, S. (1996). Faithful representation of similarities among three-dimensional shapes in human vision. Proc. Natl. Acad. Sci. U.S.A. 93, 12046-12050.
26. Cutzu, F., and Edelman, S. (1998). Representation of object similarity in human vision: psychophysics and a computational model. Vision Res. 38, 2227-2257.
27. DeMers, D., and Cottrell, G. (1993). “Nonlinear dimensionality reduction,” in Advances in Neural Information Processing Systems 5, eds S. J. Hanson, J. D. Cowan, and C. L. Giles (Washington, DC: Morgan Kaufmann), 580-587.
28. Dennett, D. C. (2003). Freedom Evolves. New York, NY: Viking.
29. Dewey, J. (1910). How We Think. Lexington, MA: D. C. Heath.
30. DiCarlo, J. J., and Cox, D. D. (2007). Untangling invariant object recognition. Trends Cogn. Sci. 11, 333-341.
31. DiCarlo, J. J., and Maunsell, J. H. R. (2003). Anterior inferotemporal neurons of monkeys engaged in object recognition can be highly sensitive to object retinal position. J. Neurophysiol. 89, 3264-3278.
32. Dill, M., and Edelman, S. (2001). Imperfect invariance to object translation in the discrimination of complex shapes. Perception 30, 707-724.
33. Divvala, S. K., Hoiem, D., Hays, J., Efros, A. A., and Hebert, M. (2009). “An empirical study of context in object detection,” in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), (Cambridge, MA).
34. Duvdevani-Bar, S., and Edelman, S. (1999). Visual recognition and categorization on the basis of similarities to multiple class prototypes. Int. J. Comput. Vis. 33, 201-228.
35. Duvdevani-Bar, S., Edelman, S., Howell, A. J., and Buxton, H. (1998). “A similarity-based method for the generalization of face recognition over pose and expression,” in Proceedings of the 3rd International Symposium on Face and Gesture Recognition (FG98), eds S. Akamatsu and K. Mase (Washington, DC), 118-123.
36. Edelman, S. (1995). Representation, similarity, and the chorus of prototypes. Minds Mach. 5, 45-68.
37. Edelman, S. (1998). Representation is representation of similarity. Behav. Brain Sci. 21, 449-498.
38. Edelman, S. (1999). Representation and Recognition in Vision. Cambridge, MA: MIT Press.
39. Edelman, S. (2008). Computing the Mind: How the Mind Really Works. New York, NY: Oxford University Press.
40. Edelman, S., and Bülthoff, H. H. (1992). Orientation dependence in the recognition of familiar and novel views of 3D objects. Vision Res. 32, 2385-2400.
41. Edelman, S., and Intrator, N. (2000). (Coarse coding of shape fragments) + (retinotopy) ≈ representation of structure. Spat. Vis. 13, 255-264.
42. Edelman, S., and Intrator, N. (2002). “Models of perceptual learning,” in Perceptual Learning, eds M. Fahle and T. Poggio (Berlin: MIT Press), 337-353.
43. Edelman, S., and Intrator, N. (2003). Towards structural systematicity in distributed, statically bound visual representations. Cogn. Sci. 27, 73-109.
44. Edelman, S., and Shahbazi, R. (2011). Survival in a world of probable objects. Behav. Brain Sci. 34, 197-198. A commentary on Bayesian Fundamentalism or Enlightenment? On the Explanatory Status and Theoretical Contributions of Bayesian Models of Cognition by Jones and Love.
45. Edelman, S., and Duvdevani-Bar, S. (1997). “Similarity-based viewspace interpolation and the categorization of 3D objects,” in Proceedings of the Similarity and Categorization Workshop, (Department of AI, University of Edinburgh), 75-81.
46. Edelman, S., Bulthoff, H. H., and Bulthoff, I. (1999). Effects of parametric manipulation of inter-stimulus similarity on 3D object recognition. Spat. Vis. 12, 107-123.
47. Edelman, S., Grill-Spector, K., Kushnir, T., and Malach, R. (1998). Towards direct visualization of the internal shape representation space by fMRI. Psychobiology 26, 309-321.
48. Edelman, S., Intrator, N., and Jacobson, J. S. (2002). “Unsupervised learning of visual structure,” in Proceedings of the 2nd International Workshop on Biologically Motivated Computer Vision, Volume 2525 of Lecture Notes in Computer Science, eds H. H. Bülthoff, C. Wallraven, S.-W. Lee, and T. Poggio (New York, NY: Springer), 629-643.
49. Eisler, H. (1960). Similarity in the continuum of heaviness with some methodological and theoretical considerations. Scand. J. Psychol. 1, 69-81.
50. Epstein, R., and Kanwisher, N. (1998). A cortical representation of the local visual environment. Nature 392, 598-601.
51. Eshghi, K., and Rajaram, S. (2008). “Locality sensitive hash functions based on concomitant rank order statistics,” in The 14th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD08), (New York, NY).
52. Fischler, M. A., and Bolles, R. C. (1981). Random sample consensus: a paradigm for model fitting with applications to image analysis and automated cartography. Commun. ACM 24, 381-395.
53. Frege, G. (1891). “On sense reference,” in Translations from the Philosophical Writings of G., Frege, eds P. Geach and M. Black (Oxford: Blackwell). Translated as “On Sense and Meaning” (1993), 56-78.
54. Gallant, J. L., Shoup, R. E., and Mazer, J. A. (2000). A human extrastriate area functionally homologous to Macaque V4. Neuron 27, 227-235.
55. Garner, W. R., and Felfoldy, G. L. (1970). Integrality of stimulus dimensions in various types of information processing. Cogn. Psychol. 1, 225-241.
56. Giese, M. A., Thornton, I., and Edelman, S. (2008). Metrics of the perception of body movement. J. Vis. 8, 1-18.
57. Goodman, N. (1972). Seven Strictures on Similarity. Indianapolis, IN: Bobbs Merill.
58. Hahn, U., and Chater, N. (1998). “Similarity and rules: distinct? exhaustive? empirically distinguishable?,” in Similarity and symbols in human thinking, eds S. A. Sloman and L. J. Rips (Cambridge, MA: MIT Press), 111-144.
59. Haussler, D. (1992). Decision theoretic generalizations of the PAC model for neural net and other learning applications. Inf. Comput. 100, 78-150.
60. Hayworth, K. J., Lescroart, M. D., and Biederman, I. (2011). Neural encoding of relative position. J. Exp. Psychol. Hum. Percept. Perform. 37, 1032-1050.
61. He, J., Liu, W., and Chang, S.-F. (2010). “Scalable similarity search with optimized kernel hashing,” in Proceedings of the Knowledge Discovery and Data Mining (KDD'10), (Boston, MA). 1129-1138.
62. Huber, P. J. (1985). Projection pursuit (with discussion). Ann. Stat. 13, 435-475.
63. Hume, D. (1748). An Enquiry Concerning Human Understanding. Available online at http://eserver.org/18th/hume-enquiry.html.
64. Hummel, J. E. (2001). Complementary solutions to the binding problem in vision: implications for shape perception and object recognition. Vis. Cogn. 8, 489-517.
65. Hummel, J. E., and Holyoak, K. J. (1998). Distributed representations of structure: a theory of analogical access and mapping. Psychol. Rev. 104, 427-466.
66. Huttenlocher, D. P., and Ullman, S. (1987). “Object recognition using alignment,” in Proceedings of the 1st International Conference on Computer Vision, (London, England; Washington, DC: IEEE), 102-111.
67. Intrator, N., and Cooper, L. N. (1992). Objective function formulation of the BCM theory of visual cortical plasticity: statistical connections, stability conditions. Neural Netw. 5, 3-17.
68. Intrator, N., and Edelman, S. (1996). How to make a low-dimensional representation suitable for diverse tasks. Connect. Sci. 8, 205-224.
69. Intrator, N., and Edelman, S. (1997). Learning low dimensional representations of visual objects with extensive use of prior knowledge. Network 8, 259-281.
70. Johnson, K. E. (2004). On the systematicity of language and thought. J. Philos. 111-139.
71. Johnson, W. B., and Lindenstrauss, J. (1984). Extensions of Lipschitz mappings into a Hilbert space. Contemp. Math. 26, 189-206.
72. Jones, M. N., and Mewhort, D. J. K. (2007). Representing word meaning and order information in a composite holographic lexicon. Psychol. Rev. 114, 137.
73. Kemp, C., Bernstein, A., and Tenenbaum, J. B. (2005). “A generative theory of similarity,” in Proceedings of the Twenty-Seventh Annual Conference of the Cognitive Science Society, (Washington, DC).
74. Kravitz, D. J., Vinson, L. D., and Baker, C. I. (2008). How position dependent is visual object recognition? Trends Cogn. Sci. 12, 114-122.
75. Kulis, B., and Grauman, K. (2009). “Kernelized locality-sensitive hashing for scalable image search,” in Proceedings of the 12th International Conference on Computer Vision (ICCV), 2130-2137.
76. Kulkarni, G., Premraj, V., Dhar, S., Li, S., Choi, Y., Berg, A. C., and Berg, T. L. (2011). “Baby talk: understanding and generating simple image descriptions,” in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), eds T. Boult, T. Kanade, and S. Peleg, (Colorado Springs, CO).
77. Lamdan, Y., and Wolfson, H. (1988). “Geometric hashing: a general and efficient recognition scheme,” in Proceedings of the 2nd International Conference on Computer Vision, (Tarpon Springs, FL; Washington, DC: IEEE), 238-251.
78. Lando, M., and Edelman, S. (1995). Receptive field spaces and class-based generalization from a single view in face recognition. Network 6, 551-576.
79. Lazebnik, S., Schmid, C., and Ponce, J. (2006). “Beyond bags of features: spatial pyramid matching for recognizing natural scene categories,” in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), (Washington, DC).
80. Li, P., and König, A. C. (2011). Theory and applications of b-bit minwise hashing. Commun. ACM 54, 101-109.
81. Linde, Y., Buzo, A., and Gray, R. (1980). An algorithm for vector quantizer design. IEEE Trans. Commun. 28, 84-95.
82. Loeff, N., and Farhadi, A. (2008). “Scene discovery by matrix factorization,” in Proceedings of the European Conference on Computer Vision (ECCV), (New York, NY).
83. Logothetis, N. K., Pauls, J., Poggio, T., and Bülthoff, H. H. (1994). View dependent object recognition by monkeys. Curr. Biol. 4, 404-441.
84. Logothetis, N., and Pauls, J. (1995). Psychophysical and physiological evidence for viewer-centered object representations in the primate. Cereb. Cortex 3, 270-288.
85. Lowe, D. G. (1999). “Object recognition from local scale-invariant features,” in Proceedings ofthe International Conference on Computer Vision, Vol. 2. (Washington, DC), 1150-1157.
86. MacEvoy, S. P., and Epstein, R. A. (2011). Constructing scenes from objects in human occipitotemporal cortex. Nat. Neurosci. 14, 1323-1331.
87. Malisiewicz, T., and Efros, A. A. (2009). “Beyond categories: the visual Memex model for reasoning about object relationships,” in Proceedings of the 22nd Neural Information Processing Systems Conference (NIPS), eds Y. Bengio, D. Schuurmans, J. Lafferty, C. K. I. Williams, and A. Culotta, 1222-1230.
88. Malisiewicz, T., Gupta, A., and Efros, A. A. (2011). “Ensemble of exemplar-SVMs for object detection and beyond,” in Proceedings of the International Conference on Computer Vision (ICCV) 2011, eds D. Metaxas, L. Quan, A. Sanfeliu, and L. Van Cool, (Barcelona, Spain).
89. Markman, A., and Gentner, D. (1993). Structural alignment during similarity comparisons. Cogn. Psychol. 25, 431-467.
90. Marr, D. (1982). Vision. San Francisco, CA: W. H. Freeman.
91. Medin, D. L., Goldstone, R. L., and Gentner, D. (1993). Respects for similarity. Psychol. Rev. 100, 254-278.
92. Mel, B. W., and Fiser, J. (2000). Minimizing binding errors using learned conjunctive features. Neural Comput. 12, 247-278.
93. Merker, B. (2004). Cortex, countercurrent context, and dimensional integration of lifetime memory. Cortex 40, 559-576.
94. Minsky, M. (1961). Steps toward artificial intelligence. Proc. Inst. Radio Eng. 49, 8-30.
95. Moses, Y., Ullman, S., and Edelman, S. (1996). Generalization to novel images in upright and inverted faces. Perception 25, 443-462.
96. Newell, F. N., Sheppard, D., Edelman, S., and Shapiro, K. (2005). The interaction of shape- and location-based priming in object categorisation: evidence for a hybrid what+where representation stage. Vision Res. 45, 2065-2080.
97. Oliva, A., and Torralba, A. (2001). Modeling the shape of the scene: a holistic representation of the spatial envelope. Int. J. Comput. Vis. 42, 145-175.
98. Op de Beeck, H., and Vogels, R. (2000). Spatial sensitivity of Macaque inferior temporal neurons. J. Comp. Neurol. 426, 505-518.
99. Op de Beeck, H., Wagemans, J., and Vogels, R. (2001). Inferotemporal neurons represent low-dimensional configurations of parameterized shapes. Nat. Neurosci. 4, 1244-1252.
100. Panis, S., Vangeneugden, J., Op de Beeck, H. P., and Wagemans, J. (2008). The representation of subordinate shape similarity in human occipitotemporal cortex. J. Vis. 8, 1-15.
101. Paulevé, L., Jégou, H., and Amsaleg, L. (2010). Locality sensitive hashing: A comparison of hash function types and querying mechanisms. Pattern Recognit. Lett. 31, 1348-1358.
102. Perrett, D. I., and Oram, M. W. (1998). Visual recognition based on temporal cortex cells: viewer-centred processing of pattern configuration. Z. Naturforsch. 53, 518-541.
103. Plate, T. A. (1991). “Holographic reduced representations: convolution algebra for compositional distributed representations,” in Proceedings of the 12th International Joint Conference on Artificial Intelligence (IJCAI), eds J. Mylopoulos and R. Reiter (San Mateo, CA: Morgan Kaufmann), 30-35.
104. Poggio, T. (1990). A theory of how the brain might work. Cold Spring Harbor Symposia on Quantitative Biology, LV, 899-910.
105. Poggio, T., and Girosi, F. (1990). Regularization algorithms for learning that are equivalent to multilayer networks. Science 247, 978-982.
106. Poggio, T., and Edelman, S. (1990). A network that learns to recognize three-dimensional objects. Nature 343, 263-266.
107. Rao, S. C., Rainer, G., and Miller, E. K. (1997). Integration of what and where in the primate prefrontal cortex. Science 276, 821-824.
108. Rips, L. J. (1989). “Similarity, typicality, and categorization,” in Similarity, Analogy, and Thought, eds S. Vosniadu and A. Ortony (Cambridge: Cambridge University Press), 21-59.
109. Rissanen, J. (1987). “Minimum description length principle,” in Encyclopedia of Statistic Sciences, Vol. 5, eds S. Kotz and N. L. Johnson (Washington, DC: J. Wiley and Sons), 523-527.
110. Rousselet, G. A., Thorpe, S. J., and Fabre-Thorpe, M. (2003). Taking the MAX from neuronal responses. Trends Cogn. Sci. 7, 99-102.
111. Russell, B., Torralba, A., Murphy, K., and Freeman, W. T. (2008). LabelMe: a database and web-based tool for image annotation. Int. J. Comput. Vis. 77, 157-173.
112. Rust, N., and DiCarlo, J. (2010). Selectivity and tolerance (‘invariance’) both increase as visual information propagates from cortical area V4 to IT. J. Neurosci. 30, 12978-12995.
113. Sadeghi, M. A., and Farhadi, A. (2011). “Recognition using visual phrases,” in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), eds T. Boult, T. Kanade, and S. Peleg (Colorado Springs, CO).
114. Sahlgren, M., Holst, A., and Kanerva, P. (2008). “Permutations as a means to encode order in word space,” in Proceedings of the 30th Annual Meeting of the Cognitive Science Society, eds B. C., Love, K. McRae, and V. M. Sloutsky (Austin, TX: Cognitive Science Society), 1300-1305.
115. Sali, E., and Ullman, S. (1998). “Recognizing novel 3-D objects under new illumination and viewing position using a small number of example views or even a single view,” in Proceedings of the International Conference on Computer Vision (ICCV), IEEE (Washington, DC), 153-164.
116. Salinas, E., and Abbott, L. F. (1997). Invariant visual responses from attentional gain fields. J. Neurophysiol. 77, 3267-3272.
117. Salinas, E., and Thier, P. (2000). Gain modulation: a major computational principle of the central nervous system. Neuron 27, 15-21.
118. Serre, T., Oliva, A., and Poggio, T. (2008). A feedforward architecture accounts for rapid categorization. Proc. Natl. Acad. Sci. U.S.A. 104, 6424-6429.
119. Shahbazi, R., Field, D. J., and Edelman, S. (2011). “The role of hierarchy in learning to categorize images,” in Proceedings of the 33rd Cognitive Science Society Conference, eds L. Carlson, C. Holscher, and T. Shipley (Boston, MA).
120. Shakhnarovich, G. (2008). Matlab LSH Toolbox. Retrieved on 2/1/2012 from http://ttic.uchicago.edu/~gregory/code/lsh/lshcode.tar.gz.
121. Shashua, A. (1992). “Illumination and view position in 3D visual recognition,” in Neural Information Processing Systems, Vol. 4, eds J. Moody, S. J. Hanson, and R. L. Lippman (San Mateo, CA: Morgan Kaufmann), 404-411
122. Shepard, R. N. (1980). Multidimensional scaling, tree-fitting, and clustering. Science 210, 390-397.
123. Shepard, R. N. (1984). Ecological constraints on internal representation: resonant kinematics of perceiving, imagining, thinking, and dreaming. Psychol. Rev. 91, 417-447.
124. Shepard, R. N. (1987). Toward a universal law of generalization for psychological science. Science 237, 1317-1323.
125. Shepard, R. N. (2001). Perceptual-cognitive universals as reflections of the world. Behav. Brain Sci. 24, 581-601.
126. Shepard, R. N., and Chipman, S. (1970). Second-order isomorphism of internal representations: shapes of states. Cogn. Psychol. 1, 1-17.
127. Sugihara, T., Edelman, S., and Tanaka, K. (1998). Representation of objective similarity among three-dimensional shapes in the monkey. Biol. Cyber. 78, 1-7.
128. Szabó, Z. (2008). “Compositionality,” in The Stanford Encyclopedia of Philosophy, ed E. N. Zalta (online).
129. Tenenbaum, J. B., and Griffiths, T. L. (2001). Generalization, similarity, and bayesian inference. Behav. Brain Sci. 24, 629-641.
130. Thorpe, S., Fize, D., and Marlot, C. (1996). Speed of processing in the human visual system. Nature 381, 520-522.
131. Torralba, A. (2003). Context challenge: contextual priming for object detection. Int. J. Comput. Vis. 53, 169-191.
132. Townsend, J. T., and Thomas, R. D. (1993). “On the need for a general quantitative theory of pattern similarity,” in Foundations of Perceptual Theory, ed S. C. Masin, (Amsterdam: Elsevier), 297-368.
133. Tsunoda, K., Yamane, Y., Nishizaki, M., and Tanifuji, M. (2001). Complex objects are represented in macaque inferotemporal cortex by the combination of feature columns. Nat. Neurosci. 4, 832-838.
134. Tversky, A. (1977). Features of similarity. Psychol. Rev. 84, 327-352.
135. Tversky, A., and Gati, I. (1978). “Studies of similarity,” in Cognition and Categorization, eds E. Rosch and B. Lloyd (Washington, DC: Erlbaum), 79-98.
136. Ullman, S. (1989). Aligning pictorial descriptions: an approach to object recognition. Cognition 32, 193-254.
137. van Dam, W. O., and Hommel, B. (2010). How object-specific are object files? evidence for integration by location. J. Exp. Psychol. Hum. Percept. Perform. 36, 1184-1192.
138. van Gemert, J. C., Veenman, C. J., Smeulders, A. W. M., and Geusebroek, J.-M. (2010). Visual word ambiguity. IEEE Trans. Pattern Anal. Mach. Intell. 32, 1271-1283.
139. Vogels, R. (1999). Effect of image scrambling on inferior temporal cortical responses. Neuroreport 10, 1811-1816.
140. Wachsmuth, E., Oram, M. W., and Perrett, D. I. (1994). Recognition of objects and their component parts: responses of single units in the temporal cortex of the macaque. Cereb. Cortex 5, 509-522.
141. Wang, J., Yang, J., Yu, K., Lv, K., Huang, T., and Gong, Y. (2010). “Locality-constrained linear coding for image classification,” in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), (San Francisco, CA), 3360-3367.
142. Wang, Y., Fujita, I., and Murayama, Y. (2000). Neuronal mechanisms of selectivity for object features revealed by blocking inhibition in inferotemporal cortex. Nat. Neurosci. 3, 807-813.
143. Watanabe, S. (1969). Knowing and Guessing: A Quantitative Study of Inference and Information. New York, NY: Wiley.
144. Wickelgren, W. (1969). Context-sensitive coding, associative memory, and serial order in (speech) behavior. Psychol. Rev. 76, 115.
145. Willshaw, D. J., Buneman, O. P., and Longuet-Higgins, H. C. (1969). Non-holographic associative memory. Nature222, 960-962.
146. Yagnik, J., Strelow, D., Ross, D. A., and Lin, R. (2011). “The power of comparative reasoning,” in Proceedings of the International Conference on Computer Vision (ICCV'11), eds D. Metaxas, L. Quan, A. Sanfeliu, and L. Van Gool (Barcelona, Spain).
147. Zhang, Y., Meyers, E. M., Bichot, N. P., Serre, T., Poggio, T. A., and Desimone, R. (2011). Object decoding with attention in inferior temporal cortex. Proc. Natl. Acad. Sci. U.S.A. 108, 8850-8855.
148. Zhang, Y., Jia, Z., and Chen, T. (2011). “Image retrieval with geometry-preserving visual phrases,” in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR) (Providence, RI).