Classification of run-length encoded binary data

被引：9

作者：

Babu, T. Ravindra ^{[1
]}

Murty, M. Narasimha

Agrawal, V. K.

机构：

[1] Indian Inst Sci, Dept Comp Sci & Automat, Bangalore 560012, Karnataka, India

[2] ISRO, Satellite Ctr, Bangalore, Karnataka, India

来源：

PATTERN RECOGNITION | 2007年 / 40卷 / 01期

关键词：

non-lossy compression; classification of compressed data; run-length;

D O I：

10.1016/j.patcog.2006.05.002

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

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. (c) 2006 Pattern Recognition Society. Published by Elsevier Ltd. All rights reserved.

引用

页码：321 / 323

页数：3