Automatic Shape Annotation Using Rough Sets and Decision Trees

Abstract — Annotation of images automatically assigns tags to images by analyzing contents of images. Shape is the most important feature of images, by using this features tagging of images is possible, can be termed as automatic shape annotation. In this paper, a novel classifiers using machine learning techniques viz. Rough Set (RS) and Decision Tree (DT) are presented to classify shape images of a standard dataset for annotation purpose. Shape based features are extracted and organized to form a shape feature. Rough Set Exploration System (RSES) is used to develop decision tree based, rough set based classifiers for the tagging of shapes. The results obtained using these classifiers are presented and discussed. The RS classifier significantly improves the annotation performance.