A unified perspective and new results on RHT computing, mixture based learning, and multi-learner based problem solving

On one hand, multiple object detection approaches of Hough transform (HT) type and randomized HT type have been extended into an evidence accumulation featured general framework for problem solving, with five key mechanisms elaborated and several extensions of HT and RHT presented. On the other hand...

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Bibliographic Details
Published in:Pattern recognition Vol. 40; no. 8; pp. 2129 - 2153
Main Author: Xu, Lei
Format: Journal Article
Language:English
Published: Elsevier Ltd 01.08.2007
Subjects:
Automatic model selection
Bayesian Ying–Yang learning
Elliptic RPCL
Evidence combination
Hough transform
Local subspaces
Mixture of experts
Multi-sets modelling
Object detection
RBF nets
Rival penalized competitive learning (RPCL)
Multi-sets modelling
Evidence combination
Mixture of experts
Bayesian Ying–Yang learning
RBF nets
Automatic model selection
Object detection
Elliptic RPCL
Local subspaces
Hough transform
Rival penalized competitive learning (RPCL)
ISSN:0031-3203, 1873-5142
Online Access:Get full text
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Summary:On one hand, multiple object detection approaches of Hough transform (HT) type and randomized HT type have been extended into an evidence accumulation featured general framework for problem solving, with five key mechanisms elaborated and several extensions of HT and RHT presented. On the other hand, another framework is proposed to integrate typical multi-learner based approaches for problem solving, particularly on Gaussian mixture based data clustering and local subspace learning, multi-sets mixture based object detection and motion estimation, and multi-agent coordinated problem solving. Typical learning algorithms, especially those that base on rival penalized competitive learning (RPCL) and Bayesian Ying–Yang (BYY) learning, are summarized from a unified perspective with new extensions. Furthermore, the two different frameworks are not only examined with one viewed crossly from a perspective of the other, with new insights and extensions, but also further unified into a general problem solving paradigm that consists of five basic mechanisms in terms of acquisition, allocation, amalgamation, admission, and affirmation, or shortly A5 paradigm.
ISSN:0031-3203
1873-5142
DOI:10.1016/j.patcog.2006.12.016