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@InProceedings{CostaHumpTrai:2012:EfAlFr,
               author = "Costa, Alceu Ferraz and Humpire-Mamani, Gabriel and Traina, Agma 
                         Juci Machado",
          affiliation = "University of S{\~a}o Paulo, USP, Department of Computer Science 
                         and University of S{\~a}o Paulo, USP, Department of Computer 
                         Science and University of S{\~a}o Paulo, USP, Department of 
                         Computer Science",
                title = "An Efficient Algorithm for Fractal Analysis of Textures",
            booktitle = "Proceedings...",
                 year = "2012",
               editor = "Freitas, Carla Maria Dal Sasso and Sarkar, Sudeep and Scopigno, 
                         Roberto and Silva, Luciano",
         organization = "Conference on Graphics, Patterns and Images, 25. (SIBGRAPI)",
            publisher = "IEEE Computer Society",
              address = "Los Alamitos",
             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.",
  conference-location = "Ouro Preto",
      conference-year = "Aug. 22-25, 2012",
             language = "en",
           targetfile = "PID2438001.pdf",
        urlaccessdate = "2021, Jan. 24"
}


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