Abstract
Gaussian mixture models (GMM) are widely used for image segmentation. The bigger the number in the mixture, the higher will be the data likelihood. Unfortunately, too many GMM components leads to model overfitting and poor segmentation. Thus, there has been a growing interest in GMM reduction algorithms that rely on component fusion while preserving the structure of data. In this work, we present an algorithm based on a closed-form Cauchy-Schwarz divergence for GMM reduction. Contrarily to previous GMM reduction techniques which a single GMM, our approach can lead to multiple small GMMs describing more accurately the structure of the data. Experiments on image foreground segmentation demonstrate the effectiveness of our proposed model compared to state-of-art methods.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Achanta, R., Shaji, A., et al.: SLIC superpixels compared to state-of-the-art superpixel methods. IEEE TPAMI 34(11), 2274–2282 (2012)
Allili, M.S., Ziou, D., et al.: Image and video segmentation by combining unsupervised generalized Gaussian mixture modeling and feature selection. IEEE TCSVT 20(10), 1373–1377 (2010)
Allili, M.S.: Effective object tracking by matching object and background models using active contours. In: IEEE ICIP, pp. 873–876 (2009)
Allili, M.S., Ziou, D.: Automatic colour-texture image segmentation using active contours. Int. J. Comput. Math. 84(9), 1325–1338 (2007)
Boulmerka, A., Allili, M.S., et al.: A generalized multiclass histogram thresholding approach based on mixture modelling. PR 47(3), 1330–1348 (2014)
Chen, H.D., Chang, K.C., Smith, C.: Constraint optimized weight adaptation for Gaussian mixture reduction. In: Signal Processing, Sensor Fusion, and Target Recognition, SPIE, vol. 7697 (2010)
Cheng, M.-M., Mitra, N.J., et al.: Global contrast based salient region detection. IEEE TPAMI 37(3), 569–582 (2015)
Crouse, D.F., Willett, P., et al.: A look at Gaussian mixture reduction algorithms. In: IEEE International Conference on Information Fusion, pp. 1–8 (2011)
Dempster, A.P., Laird, N.M., et al.: Maximum likelihood from incomplete data via the EM algorithm. J. Roy. Stat. Soc. B 39(1), 1–38 (1977)
Filali, I., Allili, M.S., et al.: Multi-scale salient object detection using graph ranking and global-local saliency refinement. Sig. Process. Image Commun. 47, 380–401 (2016)
Figueiredo, M.T., Jain, A.K.: Unsupervised learning of finite mixture models. IEEE TPAMI 24(3), 381–396 (2002)
Fu, Z., Wang, L.: Color image segmentation using Gaussian mixture model and EM algorithm. In: Wang, F.L., Lei, J., Lau, R.W.H., Zhang, J. (eds.) CMSP 2012. CCIS, vol. 346, pp. 61–66. Springer, Heidelberg (2012). https://doi.org/10.1007/978-3-642-35286-7_9
Goldberger, J., Roweis, S.T.: Hierarchical clustering of a mixture model. In: NIPS, pp. 505–512 (2005)
Hershey, J.R., Olsen, P.: Approximating the Kullback-Leibler divergence between Gaussian mixture models. In: IEEE ICASSP, pp. 317–320 (2007)
ISIC: The International Skin Imaging Collaboration. https://challenge2018.isic-archive.com/. Accessed 24 May 2019
Kampa, K., Hasanbelliu, E., Principe, J.: Closed-form Cauchy-Schwarz PDF divergence for mixture of Gaussians. In: IEEE IJCNN, pp. 2578–2585 (2011)
Runnalls, A.R.: Kullback-Leibler approach to Gaussian mixture reduction. IEEE TAES 43(3), 989–999 (2007)
Acknowledgment
The authors would like to thank the Natural Sciences and Engineering Research Council of Canada (NSERC) for their support.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2019 Springer Nature Switzerland AG
About this paper
Cite this paper
Nouboukpo, A., Allili, M.S. (2019). Spatially-Coherent Segmentation Using Hierarchical Gaussian Mixture Reduction Based on Cauchy-Schwarz Divergence. In: Karray, F., Campilho, A., Yu, A. (eds) Image Analysis and Recognition. ICIAR 2019. Lecture Notes in Computer Science(), vol 11662. Springer, Cham. https://doi.org/10.1007/978-3-030-27202-9_35
Download citation
DOI: https://doi.org/10.1007/978-3-030-27202-9_35
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-27201-2
Online ISBN: 978-3-030-27202-9
eBook Packages: Computer ScienceComputer Science (R0)