Object-based image similarity computation using inductive learning of contour-segment relations

This paper describes an efficient and effective image similarity calculation method for object-based image comparison at the level of object classes. It uses probabilistic-prediction voting based on the predicted class distribution of each segment of the contour of an object in an image to determine the class of the object. The C4.5 inductive learning algorithm is used to predict the class distribution of object-contour segments. This method is invariant to rotation, scaling and translation of objects. Experimental results show that the method is effective and efficient. It can be used for object-based image retrieval.