Object retrieval based on spatially frequent items with informative patches
Spatial relation of local image patches plays an important role in object-based image retrieval. An approach called spatial frequent items is proposed as an extension of Bag-of-Words method by introducing spatial relations between patches. Spatial frequent items are defined as frequent pairs of adjacent local image patches in polar coordinates, and exploited using data mining. Based on these frequent configurations, we develop a method to encode patches and their spatial relations for image indexing and retrieval. Besides, to avoid the interference of background patches, informative patches are filtrated based on their local entropy and self-similarity in the preprocess stage. Experimental results demonstrate that our method can be 8.6% more effective than the state-of-art object retrieval methods.