NDLI: An Efficient Algorithm for Fractal Analysis of Textures

Content Provider	IEEE Xplore Digital Library
Author	Costa, A.F. Humpire-Mamani, G. Traina, A.J.M.
Copyright Year	2012
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.
Starting Page	39
Ending Page	46
File Size	1713223
Page Count	8
File Format	PDF
ISBN	9781467328029
ISSN	15301834
e-ISBN	9780769548296
DOI	10.1109/SIBGRAPI.2012.15
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2012-08-22
Publisher Place	Brazil
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Feature extraction Fractals Image segmentation Vectors Computed tomography Lungs Gray-scale image processing Fractal analysis texture feature extraction content based image retrieval image classification
Content Type	Text
Resource Type	Article

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