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<identifier>oai:HAL:hal-00602268v1</identifier>
<datestamp>2018-01-11</datestamp>
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<publisher>HAL CCSD</publisher>
<title lang=en>Texture features and segmentation based on multifractal approach</title>
<creator>Grandchamp, Enguerran</creator>
<creator>Mohamed, Abadi</creator>
<contributor>Laboratoire de Mathématiques Informatique et Applications (LAMIA) ; Université des Antilles et de la Guyane (UAG)</contributor>
<contributor>SIGNAL-IMAGE-COMMUNICATION (SIC) ; Université de Poitiers - Centre National de la Recherche Scientifique (CNRS)</contributor>
<description>International audience</description>
<source>CIARP Proceedings</source>
<source>CIARP</source>
<coverage>Cancun, Mexico</coverage>
<identifier>hal-00602268</identifier>
<identifier>https://hal.archives-ouvertes.fr/hal-00602268</identifier>
<source>https://hal.archives-ouvertes.fr/hal-00602268</source>
<source>CIARP, Nov 2006, Cancun, Mexico. pp.1, 2006</source>
<language>en</language>
<subject lang=en>Multifractal theory</subject>
<subject lang=en>multifractal spectrum</subject>
<subject lang=en>wavelets</subject>
<subject lang=en>texture segmentation</subject>
<subject lang=en>high and very high spatial resolution image</subject>
<subject>[INFO.INFO-TI] Computer Science [cs]/Image Processing</subject>
<type>info:eu-repo/semantics/conferenceObject</type>
<type>Conference papers</type>
<description lang=en>In this paper, we use a multifractal approach based on the computation of two spectrums for image analysis and texture segmentation problems. The two spectrums are the Legendre Spectrum, determined by classical methods, and the Large Deviation Spectrum, determined by kernel density estimation. We propose a way for the fusion of these two spectrums to improve textured image segmentation results. An unsupervised k-means is used as clustering approach for the texture classification. The algorithm is applied on mosaic image built using IKONOS images and various natural textures from the Brodatz album. The segmentation obtained with our approach gives better results than the application of each spectrum separately.</description>
<date>2006-11-14</date>
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