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Proceedings Paper

Arranging the order of feature-extraction operations in pattern classification
Author(s): Shu-Yuen Hwang; Ronlon Tsai
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Paper Abstract

The typical process of statistical pattern classification is to first extract features from an object presented in an input image, then using the Bayesian decision rule, to compute the a posteriori probabilities that the object will be recognized by the system. When recursive Bayesian decision rule is used in this process, the phase of feature-extraction can be mixed with the phase of classification such that the a posteriori probabilities after adding each feature can be computed one by one. There are two reasons for thinking about which feature should be extracted first and which should go next. First, feature extraction is usually very time consuming. The extraction of any global feature from an object at least needs time in the order of the size of the object. Second, it is very often that we do not need to use all features in order to obtain a final classification; the a posteriori probabilities of some models will become zero after only a few features have been used. The problem is how to arrange the order of feature-extraction operations such that we can use a minimum order of operations to do the right classification. This paper presents two information-theoretical based heuristics for predicting the performance of feature-extraction operations. The prediction is then used to arrange the order of these operations. The first heuristic is the power of discrimination of each operation. The second heuristic is the power of justification of each operation and is used in the special case that some points in the feature space do not belong to any model. Both heuristics are computed from the distributions of models. The experimental result and its comparison to our previous works will be presented.

Paper Details

Date Published: 1 February 1992
PDF: 9 pages
Proc. SPIE 1609, Model-Based Vision Development and Tools, (1 February 1992); doi: 10.1117/12.57120
Show Author Affiliations
Shu-Yuen Hwang, National Chiao Tung Univ. (Taiwan)
Ronlon Tsai, Industrial Technology Research Institute (Taiwan)

Published in SPIE Proceedings Vol. 1609:
Model-Based Vision Development and Tools
Rodney M. Larson; Hatem N. Nasr, Editor(s)

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