ARCHETYPAL ANALYSIS: AN ALTERNATIVE TO CLUSTERING FOR UNSUPERVISED TEXTURE SEGMENTATION

Ismael Cabero; Irene Epifanio

doi:10.5566/ias.2052

Authors

Ismael Cabero Department of Didactics of Mathematics, Universitat de Valencia
Irene Epifanio Department of Mathematics- IMAC, Universitat Jaume I http://orcid.org/0000-0002-6973-311X

DOI:

https://doi.org/10.5566/ias.2052

Keywords:

archetype, image segmentation, local granulometries, mathematical morphology, texture analysis

Abstract

Texture segmentation is one of the main tasks in image applications, specifically in remote sensing, where the objective is to segment high-resolution images of natural landscapes into different cover types. Often the focus is on the selection of discriminant textural features, and although these are really fundamental, there is another part of the process that is also influential, partitioning different homogeneous textures into groups. A methodology based on archetype analysis (AA) of the local textural measurements is proposed. AA seeks the purest textures in the image and it can find the borders between pure textures, as those regions composed of mixtures of several archetypes. The proposed procedure has been tested on a remote sensing image application with local granulometries, providing promising results.

References

Bauckhage C, Kersting K, Hoppe F, Thurau C (2015). Archetypal Analysis as an Autoencoder. In: Workshop New Challenges in Neural Computation.

Benediktsson J, Palmason J, Sveinsson J (2005). Classification of hyperspectral data from urban areas based on extended morphological profiles. IEEE Geosci Remote 43:480–91.

Chen Y, Mairal J, Harchaoui Z (2014). Fast and Robust Archetypal Analysis for Representation Learning. In: PROC CVPR IEEE.

Cuesta-Albertos JA, Gordaliza A, Matrán C (1997). Trimmed k-means: an attempt to robustify quantizers. Ann Stat 25:553–76.

Cutler A, Breiman L (1994). Archetypal Analysis. Technometrics 36:338–47.

Dougherty ER, Kraus EJ, Pelz JB (1989). Image segmentation by local morphological granulometries. In: 12th Canadian Symposium on Remote Sensing Geoscience and Remote Sensing Symposium, vol. 3.

Epifanio I (2016). Functional archetype and archetypoid analysis. Comput Stat Data An 104:24 – 34.

Epifanio I, Ayala G (2002). A random set view of texture classification. IEEE T Image Process 11:859–67.

Epifanio I, Ibáñez MV, Simó A (2018a). Archetypal analysis with missing data: see all samples by looking at a few based on extreme profiles. Am Stat.

Epifanio I, Ibáñez MV, Simó A (2018b). Archetypal shapes based on landmarks and extension to handle missing data. Adv Data Anal Classi 12:705–35.

Epifanio I, Soille P (2007). Morphological texture features for unsupervised and supervised segmentations of natural landscapes. IEEE Geosci Remote 45:1074–83.

Epifanio I, Vinué G, Alemany S (2013). Archetypal analysis: contributions for estimating boundary cases in multivariate accommodation problem. Comput Ind Eng 64:757–65.

Eugster MJ, Leisch F (2009). From Spider-Man to Hero - Archetypal Analysis in R. J Stat Soft 30:1–23.

Eugster MJA (2012). Performance profiles based on archetypal athletes. Int J Perf Anal Spor 12:166–87.

Fauvel M, Benediktsson J, Chanussot J, Sveinsson J (2008). Spectral and spatial classification of hyperspectral data using SVMs and morphological profiles. IEEE Geosci Remote 46:3804 – 3814.

Fletcher ND, Evans AN (2005). Texture segmentation using area morphology local granulometries. In: Ronse C, Najman L, Decencière E, eds., Mathematical Morphology: 40 Years On. Dordrecht: Springer Netherlands.

García-Escudero LA, Gordaliza A, Matrán C (2003). Trimming tools in exploratory data analysis. J Comput Graph Stat 12:434–49.

Hinrich JL, Bardenfleth SE, Roge RE, Churchill NW, Madsen KH, Mørup M (2016). Archetypal analysis for modeling multisubject fMRI data. IEEE Journal on Selected Topics in Signal Processing 10:1160–71.

Hubert L, Arabie P (1985). Comparing partitions. J Classif 2:193–218.

Ibáñez MV, Vinué G, Alemany S, Simó A, Epifanio I, Domingo J, Ayala G (2012). Apparel sizing using trimmed PAM and OWA operators. Expert Syst Appl 39:10512 – 10520.

Jain AK, Farrokhnia F (1991). Unsupervised texture segmentation using Gabor filters. Pattern Recogn 24:1167 – 1186.

Maggi M, Estreguil C, Soille P (2007). Woody vegetation increase in alpine areas: a proposal for a classification and validation scheme. Int J Remote Sens 28:143–66.

Mair S, Boubekki A, Brefeld U (2017). Frame-based data factorizations. In: Proceedings of the 34th International Conference on Machine Learning, vol. 70 of Proceedings of Machine Learning Research.

Matheron G (1975). Random Sets and Integral Geometry. Wiley.

MATLAB (2019). Texture segmentation using Gabor filters. Accessed = 2019-04-05.

Millán-Roures L, Epifanio I, Martínez V (2018). Detection of anomalies in water networks by functional data analysis. Math Probl Eng 2018:13.

Moliner J, Epifanio I (2019). Robust multivariate and functional archetypal analysis with application to financial time series analysis. Physica A 519:195 – 208.

Mørup M, Hansen LK (2012). Archetypal analysis for machine learning and data mining. Neurocomputing 80:54–63.

Plaza A, Martinez P, Plaza J, Perez R (2005). Dimensionality reduction and classification of hyperspectral image data using sequences of extended morphological transformations. IEEE Geosci Remote 43:466–79.

R Development Core Team (2018). R: A Language and Environment for Statistical Computing. R Foundation for Statistical Computing, Vienna, Austria. ISBN 3-900051-07-0.

Ragozini G, Palumbo F, D’EspositoMR (2017). Archetypal analysis for data-driven prototype identification. Stat Anal Data Min 10:6–20.

Ramsay JO, Silverman BW (2005). Functional Data Analysis. Springer, 2nd ed.

Seth S, Eugster MJA (2016). Probabilistic archetypal analysis. Mach Learn 102:85–113.

Soille P (2003). Morphological Image Analysis: Principles and Applications. Berlin, Heidelberg: Springer, 2nd ed.

Soille P, Pesaresi M (2002). Advances in mathematical morphology applied to geoscience and remote sensing. IEEE Geosci Remote 40:2042–55.

Sun W, Yang G, Wu K, Li W, Zhang D (2017a). Pure endmember extraction using robust kernel archetypoid analysis for hyperspectral imagery. ISPRS J Photogramm 131:147 – 159.

Sun W, Zhang D, Xu Y, Tian L, Yang G, Li W (2017b). A probabilistic weighted archetypal analysis method with Earth mover’s distance for endmember extraction from hyperspectral imagery. Remote Sensing 9:841.

Thøgersen JC, Mørup M, Damkiær S, Molin S, Jelsbak L (2013). Archetypal analysis of diverse pseudomonas aeruginosa transcriptomes reveals adaptation in cystic fibrosis airways. BMC Bioinformatics 14:279.

Tsanousa A, Laskaris N, Angelis L (2015). A novel single-trial methodology for studying brain response variability based on archetypal analysis. Expert Syst Appl 42:8454 – 8462.

Tuceryan M (1994). Moment-based texture segmentation. Pattern Recogn Lett 15:659 – 668.

Tuceryan M, Jain AK (1993). Texture analysis. In: Chen CH, Pau LF, Wang PSP, eds., Handbook of Pattern Recognition & Computer Vision. River Edge, NJ, USA: World Scientific Publishing Co., Inc., 235–76.

Vinué G (2017). Anthropometry: An R package for analysis of anthropometric data. J Stat Soft 77:1–39.

Vinué G, Epifanio I (2017). Archetypoid analysis for sports analytics. Data Min Knowl Disc 31:1643–77.

Vinué G, Epifanio I, Alemany S (2015). Archetypoids: A new approach to define representative archetypal data. Comput Stat Data An 87:102 – 115.

Wang D, Haese-Coat V, Bruno A, Ronsin J (1993). Texture classification and segmentation based on iterative morphological decomposition. J Vis Commun Image R 4:197–214.