Mining viewpoint patterns in image databases

Wynne Hsu, Jing Dai, Mong-Li Lee

Abstract: The increasing number of image repositories has made image mining an important task because of its potential in discovering useful image patterns from a large set of images. In this paper, we introduce the notion of viewpoint patterns for image databases. Viewpoint patterns refer to patterns that capture the invariant relationships of one object from the point of view of another object. These patterns are unique and significant in images because the absolute positional information of objects for most images is not important, but rather, it is the relative distance and orientation of the objects from each other that is meaningful. We design a scalable and efficient algorithm to discover such viewpoint patterns. Experiments results on various image sets demonstrate that viewpoint patterns are meaningful and interesting to human users.