Algorithms for Discovery of Spatial Co-orientation Patterns from Images

Abstract

Image mining is an important task to discover interesting and meaningful patterns form large image databases. In this paper, we introduce the spatial co-orientation patterns in image databases. Spatial co-orientation patterns refer to objects that frequently occur with the same spatial orientation, e.g. left, right, below, etc. among images. For example, an object P is frequently left to an object Q among images. We utilize the data structure, 2D string, to represent the spatial orientation of objects in an image. Two approaches, Apriori-based and pattern-growth approaches, are proposed for mining co-orientation patterns. An experimental evaluation with synthetic datasets shows the advantage and disadvantage between these two algorithms.