Abstract
An improved approach for JSEG is presented for unsupervised color image segmentation. Instead of color quantization, an automatic classification method based on adaptive mean shift (AMS) based clustering is used for nonparametric clustering of image data set. The clustering results are used to construct Gaussian mixture modelling of image data set for the calculation of soft J value. The region growing algorithm used in JSEG is then applied in segmenting the image based on the multiscale soft J-images. Experiments show that the synergism of JSEG and the soft classification based on GMM overcomes the limitations of JSEG successfully and is more robust.
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© 2004 Springer-Verlag Berlin Heidelberg
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Wang, Y., Yang, J., Zhou, Y. (2004). Unsupervised Color-Texture Segmentation. In: Campilho, A., Kamel, M. (eds) Image Analysis and Recognition. ICIAR 2004. Lecture Notes in Computer Science, vol 3211. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-30125-7_14
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DOI: https://doi.org/10.1007/978-3-540-30125-7_14
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-23223-0
Online ISBN: 978-3-540-30125-7
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