Object Recognition: more than remembrance. of things past?
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1 Object Recognition: more than remembrance of things past? Shimon Edelman
2 Object Recognition: mouse monkey sheep tiger hippo dog things past thing to be recognized
3 Challenge #1: a novel view mouse monkey sheep tiger hippo dog remembered objects what if there is no exact match?
4 View Space smooth change in orientation leads to smooth change in measurement space this suggests: view space can be INTERPOLATED.
5 Recognition: a familiar object functional requirements: * near-constant response * rejection of other objects strategy: possible implementation: Radial Basis Function (RBF) interpolation. * interpolate view space; Σ * do Nearest-Neighbor
6 Categorization: a "moderately" novel object strategy: * interpolate view space; * do Nearest-Neighbor
7 Challenge #2: a "radically" novel object?? Nearest-Neighbor strategy a poor choice here; Need: representation before decision
8 A framework for the representation of shapes: measurement space view space shape space morphing
9 view space and shape space analogy: shape space <-> terrain
10 categorization, representation = localization in shape space REQUIRED: an active landmark mechanism - to ignore distance along view spaces but respond to distance along the relevant shape-space directions:
11 A Chorus of Prototypes (modules tuned to reference shapes) object specific module image Σ high-dimensional measurement space low-dimensional representation space
12 High-dimensional measurement space correlate with 250 Gaussian filters ("receptive fields") 250-dimensional vector of measurements
13 10 training objects ("reference shapes") cow cat Al General tuna L_rover Nissan F16 fly T.Rex
14 169 views/object 81 views of cow About 15 views/object were chosen (CVQ algorithm), to train the object-specific modules.
15 Test set #1: 20 objects from same QUADRUPEDS cow2 pigbaby tiger camel rhino categories as training objects FIGURES "FISH" chimp ape polar b whale killer w shark "CARS" truck VW Suzuki Subaru tank AIRCRAFT DINOSAURS F15 MiG27 Parasaurolophus Velociraptor
16 Test set #1: results The 10 D space spanned by similarities to the 10 training objects, embedded * into 2 D for visualization. Shown: 10 training objects; 5 test (novel) objects. * * * by multidimensional scaling (MDS) recognition rate: 83 98% * * * categorization: 79 85%
17 Test set #2: 10 objects randomly chosen from the butterfly frog tennis pump Beethoven database giraffe pawn manatee Fiat Toyota
18 Test set #2: results The 10 D space spanned by similarities to the 10 training objects, embedded * into 2 D for visualization. Shown: 10 training objects; 3 test (novel) objects. * by multidimensional scaling (MDS) recognition rate: 90 99% * * * * *
19 Test set #2: representation of novel objects cow cat Al Gene tuna Lrov Niss F_16 fly TRex frog shoe pump Beethoven giraffe manatee Fiat Toyota manatee tuna cow Nissan = [ 1.49, 0.84, 0.76 ]
20 A computer vision perspective ways to represent novel objects: I. Biederman Psych. Review 1987 structural decomposition similarities to prototypes the left way the right way
21 some shortcomings of structural descriptions: structural decomposition is not obvious for some simple common shapes......and is too cumbersome for some complex common ones......metric information must be represented in any case...
22 structural decomposition is very difficult to compute automatically similarities to prototypes are easier
23 A biological vision perspective N. Logothetis, J. Pauls, T. Poggio, Curr. Biol. 5:552 (1995) Σ
24 A tentative mapping of the model onto adapted from: K. Tanaka, Current Opinion in Neurobiology 2:502 (1992) the functional architecture of the inferotemporal cortex:
25 human psychophysics: similarity perception F. Cutzu & S. Edelman PNAS 93:12046 (1996) 1. parametrically manipulate stimulus shapes 2. use MDS to embed response data into 2D computer model human subjects
26 monkey psychophysics: similarity perception T. Sugihara, S. Edelman, K. Tanaka Invest. Ophthalm. Vis. Sci., 1996
27 Object Recognition: more than remembrance of things past? Yes and No. recognize categorize = remember a thing you saw before = remember a thing you haven t seen before represent things current in terms of similarities to things past Thanks to: Florin Cutzu, Sharon Duvdevani-Bar shantih shantih shantih
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