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Reference TypeConference Paper (Conference Proceedings)
Last Update2012:
Metadata Last Update2020: administrator
Citation KeyCostaHumpTrai:2012:EfAlFr
TitleAn Efficient Algorithm for Fractal Analysis of Textures
FormatDVD, On-line.
Access Date2021, Jan. 27
Number of Files1
Size2310 KiB
Context area
Author1 Costa, Alceu Ferraz
2 Humpire-Mamani, Gabriel
3 Traina, Agma Juci Machado
Affiliation1 University of São Paulo, USP, Department of Computer Science
2 University of São Paulo, USP, Department of Computer Science
3 University of São Paulo, USP, Department of Computer Science
EditorFreitas, Carla Maria Dal Sasso
Sarkar, Sudeep
Scopigno, Roberto
Silva, Luciano
Conference NameConference on Graphics, Patterns and Images, 25 (SIBGRAPI)
Conference LocationOuro Preto
DateAug. 22-25, 2012
Book TitleProceedings
PublisherIEEE Computer Society
Publisher CityLos Alamitos
Tertiary TypeFull Paper
History2012-09-20 16:45:34 :: -> administrator :: 2012
2020-02-19 02:18:27 :: administrator -> :: 2012
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Is the master or a copy?is the master
Content Stagecompleted
Content TypeExternal Contribution
KeywordsFractal analysis, texture, feature extraction, content based image retrieval, image classification, image processing.
AbstractIn 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.
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