Identity statement area | |
Reference Type | Conference Paper (Conference Proceedings) |
Site | sibgrapi.sid.inpe.br |
Identifier | 8JMKD3MGPBW34M/3C8EHNS |
Repository | sid.inpe.br/sibgrapi/2012/07.06.13.25 |
Last Update | 2012:07.06.13.25.27 alceufc@icmc.usp.br |
Metadata | sid.inpe.br/sibgrapi/2012/07.06.13.25.27 |
Metadata Last Update | 2020:02.19.02.18.27 administrator |
Citation Key | CostaHumpTrai:2012:EfAlFr |
Title | An Efficient Algorithm for Fractal Analysis of Textures  |
Format | DVD, On-line. |
Year | 2012 |
Access Date | 2021, Jan. 27 |
Number of Files | 1 |
Size | 2310 KiB |
Context area | |
Author | 1 Costa, Alceu Ferraz 2 Humpire-Mamani, Gabriel 3 Traina, Agma Juci Machado |
Affiliation | 1 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 |
Editor | Freitas, Carla Maria Dal Sasso Sarkar, Sudeep Scopigno, Roberto Silva, Luciano |
e-Mail Address | alceufc@icmc.usp.br |
Conference Name | Conference on Graphics, Patterns and Images, 25 (SIBGRAPI) |
Conference Location | Ouro Preto |
Date | Aug. 22-25, 2012 |
Book Title | Proceedings |
Publisher | IEEE Computer Society |
Publisher City | Los Alamitos |
Tertiary Type | Full Paper |
History | 2012-09-20 16:45:34 :: alceufc@icmc.usp.br -> administrator :: 2012 2020-02-19 02:18:27 :: administrator -> :: 2012 |
Content and structure area | |
Is the master or a copy? | is the master |
Content Stage | completed |
Transferable | 1 |
Content Type | External Contribution |
Keywords | Fractal analysis, texture, feature extraction, content based image retrieval, image classification, image processing. |
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. |
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data URL | http://urlib.net/rep/8JMKD3MGPBW34M/3C8EHNS |
zipped data URL | http://urlib.net/zip/8JMKD3MGPBW34M/3C8EHNS |
Language | en |
Target File | PID2438001.pdf |
User Group | alceufc@icmc.usp.br |
Visibility | shown |
Allied materials area | |
Mirror Repository | sid.inpe.br/banon/2001/03.30.15.38.24 |
Host Collection | sid.inpe.br/banon/2001/03.30.15.38 |
Notes area | |
Empty Fields | accessionnumber archivingpolicy archivist area callnumber copyholder copyright creatorhistory descriptionlevel dissemination documentstage doi edition electronicmailaddress group holdercode isbn issn label lineage mark nextedition nexthigherunit notes numberofvolumes orcid organization pages parameterlist parentrepositories previousedition previouslowerunit progress project readergroup readpermission resumeid rightsholder secondarydate secondarykey secondarymark secondarytype serieseditor session shorttitle sponsor subject tertiarymark type url versiontype volume |
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