Abstract
We present an algorithm for automatic selection of features that best segment an image in texture homogeneous regions. The set of “best extractors” are automatically selected among the Gabor filters, Co-occurrence matrix, Law’s energies and intensity response. Noise-features elimination is performed by taking into account the magnitude and the granularity of each feature image, i.e. the compute image when a specific feature extractor is applied. Redundant features are merged by means of probabilistic rules that measure the similarity between a pair of image feature. Then, cascade applications of general purpose image segmentation algorithms (K-Means, Graph-Cut and EC-GMMF) are used for computing the final segmented image. Additionally, we propose an evolutive gradient descent scheme for training the method parameters for a benchmark image set. We demonstrate by experimental comparisons, with stat of the art methods, a superior performance of our technique.
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Ramírez, J., Rivera, M. (2006). Probabilistic Rules for Automatic Texture Segmentation. In: Gelbukh, A., Reyes-Garcia, C.A. (eds) MICAI 2006: Advances in Artificial Intelligence. MICAI 2006. Lecture Notes in Computer Science(), vol 4293. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11925231_74
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DOI: https://doi.org/10.1007/11925231_74
Publisher Name: Springer, Berlin, Heidelberg
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