Please use this identifier to cite or link to this item: http://ktisis.cut.ac.cy/handle/10488/7233
Title: Pattern classification using a linear associative memory
Authors: Eichmann, George 
Kasparis, Takis 
Keywords: Pattern recognition
Classification
Information retrieval
Image processing
Issue Date: 1989
Publisher: Pergamon
Source: Pattern Recognition, 1989, Volume 22, Issue 6, Pages 733-740
Abstract: Pattern classification is a very important image processing task. A typical pattern classification algorithm can be broken into two parts; first, the pattern features are extracted and, second, these features are compared with a stored set of reference features until a match is found. In the second part, usually one of the several clustering algorithms or similarity measures is applied. In this paper, a new application of linear associative memory (LAM) to pattern classification problems is introduced. Here, the clustering algorithms or similarity measures are replaced by a LAM matrix multiplication. With a LAM, the reference features need not be separately stored. Since the second part of most classification algorithms is similar, a LAM standardizes the many clustering algorithms and also allows for a standard digital hardware implementation. Computer simulations on regular textures using a feature extraction algorithm achieved a high percentage of successful classification. In addition, this classification is independent of topological transformations.
URI: http://ktisis.cut.ac.cy/handle/10488/7233
ISSN: 00313203
DOI: 10.1016/0031-3203(89)90009-5
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