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		<citationkey>CostaHumpTrai:2012:EfAlFr</citationkey>
		<title>An Efficient Algorithm for Fractal Analysis of Textures</title>
		<format>DVD, On-line.</format>
		<year>2012</year>
		<numberoffiles>1</numberoffiles>
		<size>2310 KiB</size>
		<author>Costa, Alceu Ferraz,</author>
		<author>Humpire-Mamani, Gabriel,</author>
		<author>Traina, Agma Juci Machado,</author>
		<affiliation>University of São Paulo, USP, Department of Computer Science</affiliation>
		<affiliation>University of São Paulo, USP, Department of Computer Science</affiliation>
		<affiliation>University of São Paulo, USP, Department of Computer Science</affiliation>
		<editor>Freitas, Carla Maria Dal Sasso,</editor>
		<editor>Sarkar, Sudeep,</editor>
		<editor>Scopigno, Roberto,</editor>
		<editor>Silva, Luciano,</editor>
		<e-mailaddress>alceufc@icmc.usp.br</e-mailaddress>
		<conferencename>Conference on Graphics, Patterns and Images, 25 (SIBGRAPI)</conferencename>
		<conferencelocation>Ouro Preto</conferencelocation>
		<date>Aug. 22-25, 2012</date>
		<booktitle>Proceedings</booktitle>
		<publisher>IEEE Computer Society</publisher>
		<publisheraddress>Los Alamitos</publisheraddress>
		<tertiarytype>Full Paper</tertiarytype>
		<transferableflag>1</transferableflag>
		<contenttype>External Contribution</contenttype>
		<keywords>Fractal analysis, texture, feature extraction, content based image retrieval, image classification, image processing.</keywords>
		<abstract>In this paper we propose a new and efficient texture feature extraction method: the Segmentation-based Fractal Texture Analysis, or SFTA. The extraction algorithm consists in decomposing the input image into a set of binary images from which the fractal dimensions of the resulting regions are computed in order to describe segmented texture patterns. The decomposition of the input image is achieved by the Two-Threshold Binary Decomposition (TTBD) algorithm, which we also propose in this work. We evaluated SFTA for the tasks of content-based image retrieval (CBIR) and image classification, comparing its performance to that of other widely employed feature extraction methods such as Haralick and Gabor filter banks. SFTA achieved higher precision and accuracy for CBIR and image classification. Additionally, SFTA was at least 3.7 times faster than Gabor and 1.6 times faster than Haralick with respect to feature extraction time.</abstract>
		<language>en</language>
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