Splitting Rules using Intervals for Object Classification in Image Databases 


Vol. 12,  No. 6, pp. 829-836, Dec.  2005
10.3745/KIPSTD.2005.12.6.829


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  Abstract

The way to assign a splitting criterion for correct object classification is the main issue in all decisions trees. This paper describes new splitting rules for classification in order to find an optimal split point. Unlike the current splitting rules that are provided by searching all threshold values, this paper proposes the splitting rules that are based on the probabilities of pre?assigned intervals. Our methodology provides that user can control the accuracy of tree by adjusting the number of intervals. In addition, we applied the proposed splitting rules to a set of image data that was retrieved by parameterized feature extraction to recognize image objects.

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[IEEE Style]

J. S. Cho and J. S. Choi, "Splitting Rules using Intervals for Object Classification in Image Databases," The KIPS Transactions:PartD, vol. 12, no. 6, pp. 829-836, 2005. DOI: 10.3745/KIPSTD.2005.12.6.829.

[ACM Style]

June Suh Cho and Joon Soo Choi. 2005. Splitting Rules using Intervals for Object Classification in Image Databases. The KIPS Transactions:PartD, 12, 6, (2005), 829-836. DOI: 10.3745/KIPSTD.2005.12.6.829.

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