Please use this identifier to cite or link to this item:
https://hdl.handle.net/20.500.14279/1756
Title: | Pattern classification using a linear associative memory |
Authors: | Eichmann, George Kasparis, Takis |
metadata.dc.contributor.other: | Κασπαρής, Τάκης |
Major Field of Science: | Engineering and Technology |
Field Category: | Electrical Engineering - Electronic Engineering - Information Engineering |
Keywords: | Pattern recognition;Classification;Information retrieval;Image processing |
Issue Date: | 5-Jan-1989 |
Source: | Pattern Recognition, 1989, vol. 22, no. 6, pp. 733-740 |
Journal: | Pattern recognition |
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: | https://hdl.handle.net/20.500.14279/1756 |
ISSN: | 00313203 |
DOI: | 10.1016/0031-3203(89)90009-5 |
Type: | Article |
Affiliation : | City University of New York |
Publication Type: | Peer Reviewed |
Appears in Collections: | Άρθρα/Articles |
Files in This Item:
File | Description | Size | Format | |
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pattern classification using a linear.pdf | 633.91 kB | Adobe PDF | View/Open |
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