Image Annotation Using the SimpleDecisionTree

Automatic image annotation is an important but highly challenging problem in semantic-based image retrieval. In this paper, we formulate image annotation as a supervised learning image classification problem under region-based image annotation framework. In region-based image annotation, keywords are usually associated with individual regions in the training data set. This paper applys a novel simple decision tree (SDT) algorithm in our image annotation system, which can classify a large number of training data faster and more effectively. The proposed SDT algorithm is experimented on image annotation Corel data sets. Compared to classical algorithms, SDT accelerates the operation speed of the algorithm, and the classification accuracy remains robustness. It has a good application in automatic image annotation system.