Binary image description using frequent itemsets

In this paper, a novel method for binary image comparison is presented. We suppose that the image is a set of transactions and items. The proposed method applies along rows and columns of an image; this image is represented by all frequent itemset. Firstly, the rows of the image are considered as transactions and the columns of the image are considered as items. Secondly, we considered rows as items and columns as transactions. Besides, we also apply our technique to color image; firstly we segment the image and each segmented region is considered as a binary image. The proposed method is tested on the MPEG7 database and compared with the moment’s method to show its efficiency.