Babua, Ravindra T and Murty, Narasimha M and Agrawal, VK (2007) Classification of run-length encoded binary data. In: Pattern Recognition, 40 (1). 321 -323.
Restricted to Registered users only
Download (151Kb) | Request a copy
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.
|Item Type:||Journal Article|
|Additional Information:||Copyright of this article belongs to Elsevier.|
|Keywords:||Non-lossy compression;Classification of compressed data;Run-length|
|Department/Centre:||Division of Electrical Sciences > Computer Science & Automation (Formerly, School of Automation)|
|Date Deposited:||30 Nov 2007|
|Last Modified:||19 Sep 2010 04:33|
Actions (login required)