Spatial Random Partition for Common Visual Pattern Discovery
Automatically discovering common visual patterns from a collection of images is an interesting but yet challenging task, in part because it is computationally prohibiting. Although representing images as visual documents based on discrete visual words offers advantages in computation, the performance of these word-based methods largely depends on the quality of the visual word dictionary. This paper presents a novel approach base on spatial random partition and fast word-free image matching. Represented as a set of continuous visual primitives, each image is randomly partitioned many times to form a pool of subimages. Each subimage is queried and matched against the pool, and then common patterns can be localized by aggregating the set of matched subimages. The asymptotic property and the complexity of the proposed method are given in this paper, along with many real experiments. Both theoretical studies and experiment results show its advantages.