Abstract
This work presents what we think to be the first application of Dirichlet-based Hidden Markov Models (HMM) to real-world data. Initially developed in [5], this model has only been tested on controlled synthetic data, showing promising results for classification tasks. Its capabilities on proportional data are investigated and leveraged for texture classification. Comparison to HMM with Gaussian mixtures and to nearest-neighbor classifiers is conducted and a generalized Bhattacharyya distance for series of histograms is proposed. We show that HMM with Dirichlet mixtures outperforms other tested classifiers. Using the popular bag-of-words approach, the Dirichlet-based HMM proves its ability to discriminate well between 25 textures from challenging data sets using a global dictionary of 10 words only. This seems to represent the smallest dictionary ever used to this purpose and raises the question of the need of hundreds-word dictionaries most often used in the literature for the data sets we have tested.
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References
Andrade, E.L., Blunsden, S., Fisher, R.B.: Hidden Markov models for optical flow analysis in crowds. In: Proc. ICPR, pp. 460–463 (2006)
Bertalmío, M., Vese, L.A., Sapiro, G., Osher, S.: Simultaneous structure and texture image inpainting. IEEE Transactions on Image Processing 12(8), 882–889 (2003)
Bicego, M., Castellani, U., Murino, V.: A hidden Markov model approach for appearance-based 3D object recognition. Pattern Recognition Letters 26(16), 2588–2599 (2005)
Bouguila, N., Ziou, D., Vaillancourt, J.: Unsupervised learning of a finite mixture model based on the dirichlet distribution and its application. IEEE Transactions on Image Processing 13(11), 1533–1543 (2004)
Chen, L., Barber, D., Odobez, J.M.: Dynamical dirichlet mixture model. IDIAP-RR 02, IDIAP (2007)
Cholewa, M., Glomb, P.: Estimation of the number of states for gesture recognition with hidden Markov models based on the number of critical points in time sequence. Pattern Recognition Letters 34(5), 574–579 (2013)
Dubuisson, S.: The computation of the Bhattacharyya distance between histograms without histograms. In: Proc. IPTA, pp. 373–378 (2010)
Fan, G., Xia, X.G.: Wavelet-based texture analysis and synthesis using hidden Markov models. IEEE Transactions on Circuits and Systems 1: Fundamental Theory and Applications 50(1), 106–120 (2003)
van Ginneken, B., Katsuragawa, S., ter Haar Romeny, B.M., Doi, K., Viergever, M.A.: Automatic detection of abnormalities in chest radiographs using local texture analysis. IEEE Transactions on Medical Imaging 21(2), 139–149 (2002)
Jiang, F., Wu, Y., Katsaggelos, A.K.: Abnormal event detection from surveillance video by dynamic hierarchical clustering. In: Proc. ICIP, pp. 145–148 (2007)
Lazebnik, S., Schmid, C., Ponce, J.: A sparse texture representation using local affine regions. IEEE Transactions on Pattern Analysis and Machine Intelligence 27(8), 1265–1278 (2005)
Liu, L., Fieguth, P.W., Clausi, D.A., Kuang, G.: Sorted random projections for robust rotation-invariant texture classification. Pattern Recognition 45(6), 2405–2418 (2012)
Lowe, D.G.: Object recognition from local scale-invariant features. In: Proc. ICCV (1999)
Povlow, B.R., Dunn, S.M.: Texture classification using noncausal hidden Markov models. IEEE Transactions on Pattern Analysis and Machine Intelligence 17(10), 1010–1014 (1995)
Qin, L., Zheng, Q., Jiang, S., Huang, Q., Gao, W.: Unsupervised texture classification: Automatically discover and classify texture patterns. Image and Vision Computing 26(5), 647–656 (2008)
Rabiner, L.R., Juang, B.H.: An introduction to hidden Markov models. IEEE ASSP Magazine 3(1), 4–16 (1986)
Salles, E.O.T., Ling, L.L.: Texture classification by means of hmm modeling of am-fm features. In: Proc. ICIP, pp. 182–185 (2001)
Sasaki, Y.: The truth of the f-measure. School of Computer Science. University of Manchester (2007)
Varma, M., Zisserman, A.: A statistical approach to material classification using image patch exemplars. IEEE Transactions on Pattern Analysis and Machine Intelligence 31(11), 2032–2047 (2009)
Xu, Q., Yang, J., Siyi, D.: Color texture analysis using the wavelet-based hidden Markov model. Pattern Recognition Letters 26(11), 1710–1719 (2005)
Xu, S., Fang, T., Li, D., Wang, S.: Object classification of aerial images with bag-of-visual words. IEEE Geoscience and Remote Sensing Letters 7(2), 366–370 (2010)
Xu, Y., Ji, H., Fermüller, C.: Viewpoint invariant texture description using fractal analysis. International Journal of Computer Vision 83(1), 85–100 (2009)
Yang, X., Tian, Y.: Texture representations using subspace embeddings. Pattern Recognition Letters 34(10), 1130–1137 (2013)
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Epaillard, E., Bouguila, N., Ziou, D. (2014). Classifying Textures with Only 10 Visual-Words Using Hidden Markov Models with Dirichlet Mixtures. In: Bouchachia, A. (eds) Adaptive and Intelligent Systems. ICAIS 2014. Lecture Notes in Computer Science(), vol 8779. Springer, Cham. https://doi.org/10.1007/978-3-319-11298-5_3
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DOI: https://doi.org/10.1007/978-3-319-11298-5_3
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