Abstract
A novel segmentation algorithm for natural color image is proposed. Fibonacci Lattice-based Sampling is used to get the color labels of image so as to take advantage of the traditional approaches developed for gray-scale images. Using local fuzzy homogeneity derived from color labels, texture component is calculated to characterize spatial information. Color component is obtained by peer group filtering. To avoid over-segmentation of texture areas in a color image, these color and texture components are jointly employed to group the pixels into homogenous regions by the mean shift based clustering. Finally, experiments show very promising results.
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© 2005 Springer-Verlag Berlin Heidelberg
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Yuchou, C., Yue, Z., Yonggang, W. (2005). Combined Color and Texture Segmentation Based on Fibonacci Lattice Sampling and Mean Shift. In: Kamel, M., Campilho, A. (eds) Image Analysis and Recognition. ICIAR 2005. Lecture Notes in Computer Science, vol 3656. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11559573_4
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DOI: https://doi.org/10.1007/11559573_4
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-29069-8
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