Rapid and brief communication: Classification of run-length encoded binary data

Authors:
T. Ravindra Babu;M. Narasimha Murty;V. K. Agrawal
Affiliations:
Department of Computer Science and Automation, Indian Institute of Science, Bangalore, India;Department of Computer Science and Automation, Indian Institute of Science, Bangalore, India;ISRO Satellite Centre, Bangalore, India
Venue:
Pattern Recognition
Year:
2007

Citing 1
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Abstract

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.