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Classification of run-length encoded binary data

✍ Scribed by T. Ravindra Babu; M. Narasimha Murty; V.K. Agrawal

Publisher: Elsevier Science
Year: 2007
Tongue: English
Weight: 152 KB
Volume: 40
Category: Article
ISSN: 0031-3203
DOI: 10.1016/j.patcog.2006.05.002

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✦ Synopsis

In classification of binary featured data, distance computation is carried out by considering each feature. We represent the given binary data as run-length encoded data. This would lead to a compact or compressed representation of data. Further, we propose an algorithm to directly compute the Manhattan distance between two such binary encoded patterns. We show that classification of data in such compressed form would improve the computation time by a factor of 5 on large handwritten data. The scheme is useful in large data clustering and classification which depend on distance measures.

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