Image representation and image similarity computation for images with multiple and partially occluded objects

This paper proposes an approach to object-based image retrieval for images containing multiple and partially occluded objects. In this approach, contours of objects are used to distinguish different classes of objects in images. We decompose all the contours in an image into segments and compute features from the segments. The C4.5 decision-tree learning algorithm is used to classify each segment in the images. Each image is represented in a k-dimensional space, where k is the number of classes of objects in all the images. Each dimension represents information about one of the classes. Euclidean distance between images in the k-dimensional space is adopted to compute similarities between images based on probabilities of segment classes. Experimental results show that this approach is effective.