On Statistical and Structural Feature Extraction.
SOUTHEASTERN MASSACHUSETTS UNIV NORTH DARTMOUTH DEPT OF ELECTRICAL ENGINEERING
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The problem of extracting effective features has played a major role in pattern recognition studies. Most effort, however, has been made on the selection of statistical or mathematical features. Examples of these features are the orthogonal transforms of vector measurements. Although such features may not have physical meaning, they are usually effective and computable. The main drawback with the statistical feature extraction is that it is difficult to take contextual dependence into consideration. In many patterns such as the imagery data, there is a close interrelation among various parts of a pattern. Such interrelation representing the structural properties must be taken into account to achieve the best recognition performance. In this paper we consider the structural feature extraction in a more general sense by taking into account the relationships among parts of a pattern, and the physical, perceptual, psychological, andor physiological factors which may be translated from description to mathematical expressions suitable for automatic recognition. The paper reviews the problems in both statistical and structural feature extraction, compares the feasible approaches and examines the statistical and structural mixed model for feature extraction. Author
- Statistics and Probability