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15 January 1997 Image categorization using N x M grams
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Abstract
A method for categorizing images using N X M-grams is presented. The goal of categorization is to find all images from the same category as a given query image. Some example categories are hand-written documents, printed documents, floor plans, satellite images, and fingerprints. The categorization method is based on the N-gram technique that is commonly used for determining similarity of text documents. Intuitively an N X M-gram is a small pattern in an image. The hypothesis that two images that have the same recurring patterns are likely to belong to the same category is examined. The notion of N X M-grams is defined and the process of computing an image profile in terms of the N X M-grams termed an N X M-gram vector is explained. Two similarity measures to compare images based on their N X M-gram vectors are proposed. Results of an experiment on images from various categories are presented. The two similarity measures for N X M-gram vectors are compared to each other as well as to results of categorization using a method based on color distribution features. The results show that for our test images N X M-gram based methods were more successful in finding images from the same category as a given query image than color distribution based methods.
© (1997) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Aya Soffer "Image categorization using N x M grams", Proc. SPIE 3022, Storage and Retrieval for Image and Video Databases V, (15 January 1997); https://doi.org/10.1117/12.263401
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