Efficient Mining of Spatial Co-orientation Patterns from Image Databases

Image mining is an important task to discover interesting and meaningful patterns from large image databases. We have previously introduced 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 this paper, we propose an efficient algorithm, pattern-growth approach, for mining co-orientation patterns. An experimental evaluation with synthetic datasets shows the advantage and disadvantage between pattern-growth approach and the previous a priori-based approach.