MULTIFRACTAL DETRENDED FLUCTUATION ANALYSIS FOR IMAGE TEXTURE FEATURE REPRESENTATION

被引:26
作者
Wang, Fang [1 ,2 ]
Li, Zong-Shou [3 ]
Liao, Gui-Ping [2 ]
机构
[1] Hunan Agr Univ, Coll Sci, Changsha 410128, Hunan, Peoples R China
[2] Hunan Agr Univ, Agr Informat Inst, Changsha 410128, Hunan, Peoples R China
[3] Jishou Univ, Coll Informat Sci & Engn, Jishou 416000, Peoples R China
关键词
Texture feature; multifractal analysis; multifractal detrended fluctuation analysis; noise; compression; blur; SEGMENTATION;
D O I
10.1142/S0218001414550052
中图分类号
TP18 [人工智能理论];
学科分类号
140502 [人工智能];
摘要
Multifractal theory has been widely used in different kinds of fields. In this paper, methods were proposed to extract two kinds of multifractal descriptors of gray series and two-dimensional surfaces for gray image based on the multifractal detrended fluctuation analysis. The proposed multifractal parameters can be well described by texture feature through the test of some textures. Three aspects of experiments have been conducted to verify the robustness of the proposed parameters, which include noise immunity, degree of image blurring and compression ratio. Comparisons were conducted between the proposed parameters and other kinds of texture feature parameters calculated by the standard multifractal analysis, the method of differential box counting and the methods of gray level co-occurrence matrix. Results demonstrate that the proposed exponents of H(2) and h(2) have great noise immunity and are robust to image compression and blurring.
引用
收藏
页数:20
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