Object classification using pre-assigned intervals in image databases

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. Experimental results show that our approach provides reasonable accuracy with fewer splits, provides smaller and shallower tree, and is more stable than the existing method.