Automated Classification of Bitmap Images using Decision Trees

 

Pavel Surynek* and Ivana Luksová**

 

Charles University in Prague, Faculty of Mathematics and Physics, Department of Theoretical Computer Science and Mathematical Logic, Malostranské námestí 25, Praha, 118 00, Czech Republic, (*pavel.surynek@mff.cuni.cz, **ivana.luksova@gmail.com).

 

Manuscript received June 20, 2011.
Manuscript accepted for publication August 20, 2011.

 

Abstract

The paper addresses the design of a method for automated classification of bitmap images into classes described by the user in natural language. Examples of such naturally defined classes are images depicting buildings, landscape, artistic images, etc. The proposed classification method is based on the extraction of suitable attributes from a bitmap image such as contrast, histogram, the occurrence of straight lines, etc. Extracted attributes are subsequently processed by a decision tree which has been trained in advance. A performed experimental evaluation with 5 classification classes showed that the proposed method has the accuracy of 75%–85%. The design of the method is general enough to allow the extension of the set of classification classes as well as the number of extracted attributes to increase the accuracy of classification.

Key words: Image classification, attribute extraction, decision trees, learning.

 

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NOTA

We would like to gratefully thank the Czech Science Foundation and The Ministry of Education, Youth and Sports, Czech Republic for the financial support of this work (contracts 201/09/P318 and MSM 0021620838 respectively).

 

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