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We present a novel visual representation, called local co-occurring patterns (LCPs), which consists of characteristic local features and the statistical co-occurance relations between them. The LCPs can be discovered using an associate rule mining algorithm. Experiments show that LCPs widely exist in a large image corpus, and are more discriminant than individual local features in visual categorization tasks such as subcategory and face recognition. Furthermore, state-of-the-art categorization performance was achieved on two test data-sets.