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In this paper, two block-based texture methods are proposed for content-based image retrieval (CBIR). The approaches use the Local Binary Pattern (LBP) texture feature as the source of image description. The first method divides the query and database images into equally sized blocks from which LBP histograms are extracted. Then the block histograms are compared using a relative L1 dissimilarity measure based on the Minkowski distances. The second approach uses the image division on database images and calculates a single feature histogram for the query. It sums up the database histograms according to the size of the query image and finds the best match by exploiting a sliding search window. The first method is evaluated against color correlogram and edge histogram based algorithms. The second, user interaction dependent approach is used to provide example queries. The experiments show the clear superiority of the new algorithms against their competitors.

Block-Based Methods for Image Retrieval Using Local Binary Patterns