A Computational Learning Theory of Active Object Recognition Under Uncertainty

We present some theoretical results related to the problem of actively searching a 3D scene to determine the positions of one or more pre-specified objects. We investigate the effects that input noise, occlusion, and the VC-dimensions of the related representation classes have in terms of localizing...

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Published in:	International journal of computer vision Vol. 101; no. 1; pp. 95 - 142
Main Authors:	Andreopoulos, Alexander, Tsotsos, John K.
Format:	Journal Article
Language:	English
Published:	Boston Springer US 01.01.2013 Springer Springer Nature B.V
Subjects:	Algorithmics. Computability. Computer arithmetics Analysis Applied sciences Artificial Intelligence Complexity Computation Computer Imaging Computer Science Computer science; control theory; systems Computer vision Costs Data processing. List processing. Character string processing Decision theory Exact sciences and technology Image Processing and Computer Vision Learning Localization Machine learning Memory organisation. Data processing Noise Object recognition Pattern Recognition Pattern Recognition and Graphics Pattern recognition. Digital image processing. Computational geometry Representations Searching Sensors Software Studies Theoretical computing Uncertainty Vision Vision systems Computational complexity of vision Active vision Visual search Object recognition Attention Occlusion Complexity class Computer vision Automatic classification Constraint satisfaction Pattern recognition Computational complexity Modeling Noise level Selective attention Upper bound Uncertain system Object detection Problem solving Probability learning Learning algorithm Artificial intelligence Occultation Object location
ISSN:	0920-5691, 1573-1405
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Abstract	We present some theoretical results related to the problem of actively searching a 3D scene to determine the positions of one or more pre-specified objects. We investigate the effects that input noise, occlusion, and the VC-dimensions of the related representation classes have in terms of localizing all objects present in the search region, under finite computational resources and a search cost constraint. We present a number of bounds relating the noise-rate of low level feature detection to the VC-dimension of an object representable by an architecture satisfying the given computational constraints. We prove that under certain conditions, the corresponding classes of object localization and recognition problems are efficiently learnable in the presence of noise and under a purposive learning strategy, as there exists a polynomial upper bound on the minimum number of examples necessary to correctly localize the targets under the given models of uncertainty. We also use these arguments to show that passive approaches to the same problem do not necessarily guarantee that the problem is efficiently learnable. Under this formulation, we prove the existence of a number of emergent relations between the object detection noise-rate, the scene representation length, the object class complexity, and the representation class complexity, which demonstrate that selective attention is not only necessary due to computational complexity constraints, but it is also necessary as a noise-suppression mechanism and as a mechanism for efficient object class learning. These results concretely demonstrate the advantages of active, purposive and attentive approaches for solving complex vision problems.
AbstractList	We present some theoretical results related to the problem of actively searching a 3D scene to determine the positions of one or more pre-specified objects. We investigate the effects that input noise, occlusion, and the VC-dimensions of the related representation classes have in terms of localizing all objects present in the search region, under finite computational resources and a search cost constraint. We present a number of bounds relating the noise-rate of low level feature detection to the VC-dimension of an object representable by an architecture satisfying the given computational constraints. We prove that under certain conditions, the corresponding classes of object localization and recognition problems are efficiently learnable in the presence of noise and under a purposive learning strategy, as there exists a polynomial upper bound on the minimum number of examples necessary to correctly localize the targets under the given models of uncertainty. We also use these arguments to show that passive approaches to the same problem do not necessarily guarantee that the problem is efficiently learnable. Under this formulation, we prove the existence of a number of emergent relations between the object detection noise-rate, the scene representation length, the object class complexity, and the representation class complexity, which demonstrate that selective attention is not only necessary due to computational complexity constraints, but it is also necessary as a noise-suppression mechanism and as a mechanism for efficient object class learning. These results concretely demonstrate the advantages of active, purposive and attentive approaches for solving complex vision problems. Keywords Object recognition * Visual search * Active vision * Attention * Computational complexity of vision We present some theoretical results related to the problem of actively searching a 3D scene to determine the positions of one or more pre-specified objects. We investigate the effects that input noise, occlusion, and the VC-dimensions of the related representation classes have in terms of localizing all objects present in the search region, under finite computational resources and a search cost constraint. We present a number of bounds relating the noise-rate of low level feature detection to the VC-dimension of an object representable by an architecture satisfying the given computational constraints. We prove that under certain conditions, the corresponding classes of object localization and recognition problems are efficiently learnable in the presence of noise and under a purposive learning strategy, as there exists a polynomial upper bound on the minimum number of examples necessary to correctly localize the targets under the given models of uncertainty. We also use these arguments to show that passive approaches to the same problem do not necessarily guarantee that the problem is efficiently learnable. Under this formulation, we prove the existence of a number of emergent relations between the object detection noise-rate, the scene representation length, the object class complexity, and the representation class complexity, which demonstrate that selective attention is not only necessary due to computational complexity constraints, but it is also necessary as a noise-suppression mechanism and as a mechanism for efficient object class learning. These results concretely demonstrate the advantages of active, purposive and attentive approaches for solving complex vision problems. We present some theoretical results related to the problem of actively searching a 3D scene to determine the positions of one or more pre-specified objects. We investigate the effects that input noise, occlusion, and the VC-dimensions of the related representation classes have in terms of localizing all objects present in the search region, under finite computational resources and a search cost constraint. We present a number of bounds relating the noise-rate of low level feature detection to the VC-dimension of an object representable by an architecture satisfying the given computational constraints. We prove that under certain conditions, the corresponding classes of object localization and recognition problems are efficiently learnable in the presence of noise and under a purposive learning strategy, as there exists a polynomial upper bound on the minimum number of examples necessary to correctly localize the targets under the given models of uncertainty. We also use these arguments to show that passive approaches to the same problem do not necessarily guarantee that the problem is efficiently learnable. Under this formulation, we prove the existence of a number of emergent relations between the object detection noise-rate, the scene representation length, the object class complexity, and the representation class complexity, which demonstrate that selective attention is not only necessary due to computational complexity constraints, but it is also necessary as a noise-suppression mechanism and as a mechanism for efficient object class learning. These results concretely demonstrate the advantages of active, purposive and attentive approaches for solving complex vision problems.[PUBLICATION ABSTRACT]
Audience	Academic
Author	Tsotsos, John K. Andreopoulos, Alexander
Author_xml	– sequence: 1 givenname: Alexander surname: Andreopoulos fullname: Andreopoulos, Alexander email: aandreo@us.ibm.com organization: IBM Research-Almaden – sequence: 2 givenname: John K. surname: Tsotsos fullname: Tsotsos, John K. organization: Dept. of Computer Science and Engineering, Centre for Vision Research, York University
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Keywords	Computational complexity of vision Active vision Visual search Object recognition Attention Occlusion Complexity class Computer vision Automatic classification Constraint satisfaction Pattern recognition Computational complexity Modeling Noise level Selective attention Upper bound Uncertain system Object detection Problem solving Probability learning Learning algorithm Artificial intelligence Occultation Object location
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Snippet	We present some theoretical results related to the problem of actively searching a 3D scene to determine the positions of one or more pre-specified objects. We...
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SubjectTerms	Algorithmics. Computability. Computer arithmetics Analysis Applied sciences Artificial Intelligence Complexity Computation Computer Imaging Computer Science Computer science; control theory; systems Computer vision Costs Data processing. List processing. Character string processing Decision theory Exact sciences and technology Image Processing and Computer Vision Learning Localization Machine learning Memory organisation. Data processing Noise Object recognition Pattern Recognition Pattern Recognition and Graphics Pattern recognition. Digital image processing. Computational geometry Representations Searching Sensors Software Studies Theoretical computing Uncertainty Vision Vision systems
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Title	A Computational Learning Theory of Active Object Recognition Under Uncertainty
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