Abstract
In texture segmentation it is key to develop descriptors which provide acceptable results without a significant increment of their temporal complexity. In this contribution, we propose two probabilistic texture descriptors: polarity and texture contrast. These descriptors are related to the entropy of both the local distributions of gradient orientation and magnitude. As such descriptors are scale-dependent, we propose a simple method for selecting the optimal scale. Using the features at their optimal scale, we test the performance of these measures with an adaptive version of the ACM clustering method, in which adaptation relies on the Kolmogorov-Smirnov test. Our results with only these two descriptors are very promising.
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References
Carson, C., Belongie, S., Greenspan, H., Malik, J.: Blobworld: Image segmentation using Expectation-Maximization and its application to image querying. IEEE Trans. on Pattern Analysis and Machine Intelligence (2002)
GĆ„rding, J., Lindeberg, T.: Direct Computation of Shape Cues Using Scale- Adapted Spatial Derivative Operators. International Journal of Computer VisionĀ 17(2), 163ā191 (1996)
Hofmann, T., Puzicha, J.: Statistical Models for Co-occurrence Data. MIT AIMemo 1625 Cambridge, MA (1998)
Lozano, M.A., Escolano, F.: Recognizing Indoor Images with Unsupervised Segmentation and Graph Matching. In: Garijo, F.J., Riquelme, J.-C., Toro, M. (eds.) IBERAMIA 2002. LNCS (LNAI), vol.Ā 2527, pp. 933ā942. Springer, Heidelberg (2002)
Puzicha, J.: Histogram Clustering for Unsupervised Segmentation and Image Retrieval. Pattern Recognition LettersĀ 20, 899ā909 (1999)
Jain, A., Farrokhina, F.: Unsupervised texture segmentation using gabor filters. Pattern RecognitionĀ 23, 1167ā1186 (1991)
Knutsson, H., Granlund, G.: Texture analysis using two-dimensional quadrature filters. In: IEEE Computer Society Workshop on Computer Architecture for Pattern Analysis and Image Database Management, pp. 206ā213 (1983)
Gotlieb, C.C., Kreyszig, H.E.: Texture descriptors based on cooccurrence matrices. Comp. Vision, Graph. and Image Proc.Ā 51, 70ā86 (1990)
Unser, M.: Texture Classification and Segmentation Using Wavelet Frames. IEEE Trans. Image ProcessingĀ 4(11), 1549ā1560 (1995)
Sochen, N., Kimmel, R., Malladi, R.: A General Framework for Low Level Vision. IEEE Trans on Image ProcessingĀ 7(3), 310ā318 (1998)
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Lozano, M.A., Escolano, F. (2003). Two New Scale-Adapted Texture Descriptors for Image Segmentation. In: Sanfeliu, A., Ruiz-Shulcloper, J. (eds) Progress in Pattern Recognition, Speech and Image Analysis. CIARP 2003. Lecture Notes in Computer Science, vol 2905. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-24586-5_16
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DOI: https://doi.org/10.1007/978-3-540-24586-5_16
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-20590-6
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