Improving the clustering performance of the scanning n-tuple method by using self-supervised algorithms to introduce subclasses

Author

Tambouratzis, George

Author_Institution

Inst. for Language & Speech Process., Athens, Greece

Volume

24

Issue

6

fYear

2002

fDate

6/1/2002 12:00:00 AM

Firstpage

722

Lastpage

733

Abstract

The scanning n-tuple technique (as introduced by Lucas and Amiri, 1996) is studied in pattern recognition tasks, with emphasis placed on methods that improve its recognition performance. We remove potential edge effect problems and optimize the parameters of the scanning n-tuple method with respect to memory requirements, processing speed and recognition accuracy for a case study task. Next, we report an investigation of self-supervised algorithms designed to improve the performance of the scanning n-tuple method by focusing on the characteristics of the pattern space. The most promising algorithm is studied in detail to determine its performance improvement and the consequential increase in the memory requirements. Experimental results using both small-scale and real-world tasks indicate that this algorithm results in an improvement of the scanning n-tuple classification performance

Keywords

document image processing; handwritten character recognition; pattern clustering; performance evaluation; unsupervised learning; case study; chain coding; clustering performance; edge effect problems; experimental results; handwritten character recognition; memory requirements; pattern recognition; processing speed; scanning n-tuple method; self-supervised algorithms; subclasses; Algorithm design and analysis; Character recognition; Clustering algorithms; Delay; Digital circuits; Handwriting recognition; Neural networks; Optimization methods; Pattern recognition; Random access memory;

fLanguage

English

Journal_Title

Pattern Analysis and Machine Intelligence, IEEE Transactions on

Publisher

ieee

ISSN

0162-8828

Type

jour

DOI

10.1109/TPAMI.2002.1008380

Filename

1008380