Classification of Images Using Decision Tree
Engineering and Technology Journal,
2013, Volume 31, Issue 6, Pages 728-739
AbstractIn this paper, the proposed system is based on texture features classification for multi object images by using decision tree (ID3) algorithm. The proposed system uses image segment tile base to reduce the block effect and uses (low low) Wavelet Haar to reduce image size without loss of any important information. The image texture features like (Entropy, Homogeneity, Energy, Inverse Different Moment (IDM), Contrast and Mean) are extracted from image to build database features. All the texture features extracted from the training images are coded into database features code. ID3 algorithm uses database features code for classification of images into different classes. Splitting rules for growing ID3 algorithm are Entropy, Information Gain used to build database rules, which depend on if_then format. The proposed algorithm is experimented on to test image database with 375 images for 5 classes and uses accuracy measure. In the experimental tests 88% of the images are correctly classified and the design of the proposed system in general is enough to allow other classes and extension of the set of classification classes.
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