Multi Features Content-Based Image Retrieval Using Clustering and Decision Tree Algorithm
Abstract: The classification
can be performed by using the decision tree approach. Previous researches on the
classification using the decision tree have mostly been intended to classify
text data. This paper wasintended to introduce a classification application to
the content-based image retrieval (CBIR) with multiattributes by using a
decision tree. The attributes used were the visual features of the image, i.e.
: color moments (order 1, 2 and 3), image entropy, energy and homogeneity.
K-means cluster algorithm was used to categorize each attribute. The result of
categorized data was then built into a decision tree by using C4.5. To show the
concept in application, this research built an application with main features,
i.e.: cases data input, cases list, training process and testing process to do
classification. The resulting tests of 150 rontgen data showed the training
data classification’s truth value of 75.33% and testing data classification of
55.7%.
Author: Kusrini Kusrini, M.
Dedi Iskandar, Ferry Wahyu Wibowo
Journal Code: jptkomputergg160020
