Abstract
Supervised pixel-based texture classification is usually performed in the feature space. We propose to perform this task in (dis)simil-arity space by introducing a new compression-based (dis)similarity measure. The proposed measure utilizes two dimensional MPEG-1 encoder, which takes into consideration the spatial locality and connectivity of pixels in the images. The proposed formulation has been carefully designed based on MPEG encoder functionality. To this end, by design, it solely uses P-frame coding to find the (dis)similarity among patches/images. We show that the proposed measure works properly on both small and large patch sizes. Experimental results show that the proposed approach significantly improves the performance of supervised pixel-based texture classification on Brodatz and outdoor images compared to other compression-based dissimilarity measures as well as approaches performed in feature space. It also improves the computation speed by about 40% compared to its rivals.
This is a preview of subscription content, log in via an institution.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsPreview
Unable to display preview. Download preview PDF.
References
Petrou, M., Sevilla, P.G.: Image Processing Dealing with Texture. John Wiley & Sons, West Sussex (2006)
Garcia, M., Puig, D.: Supervised texture classification by integration of multiple texture methods and evaluation windows. Image and Vision Computing 25(7), 1091–1106 (2007)
Randen, T., Husøy, J.: Filtering for texture classification: A comparative study. IEEE Trans. Pattern Analysis and Machine Intelligence 21(4), 291–310 (1999)
Melendez, J., Puig, D., Garcia, M.: Multi-level pixel-based texture classification through efficient prototype selection via normalized cut. Pattern Recognition 43(12), 4113–4123 (2010)
Mirmehdi, M., Xie, X., Suri, J.: Handbook of Texture Analysis. Imperial Collage Press, London (2008)
Ahonen, T., Pietikainen, M.: Image description using joint distribution of filter bank responses. Pattern Recognition Letters 30(4), 368–376 (2009)
Li, M., Chen, X., Li, X., Ma, B., Vitányi, P.: The similarity metric. IEEE Trans. Information Theory 50(12), 3250–3264 (2004)
Cilibrasi, R., Vitányi, P.: Clustering by compression. IEEE Trans. Information Theory 51(4), 1523–1545 (2005)
Mortensen, J., Wu, J.J., Furst, J., Rogers, J., Raicu, D.: Effect of Image Linearization on Normalized Compression Distance. In: Ślęzak, D., Pal, S.K., Kang, B.-H., Gu, J., Kuroda, H., Kim, T.-H. (eds.) SIP 2009. CCIS, vol. 61, pp. 106–116. Springer, Heidelberg (2009)
Macedonas, A., Besiris, D., Economou, G., Fotopoulos, S.: Dictionary based color image retrieval. Journal of Visual Communication and Image Representation 19(7), 464–470 (2008)
Cerra, D., Mallet, A., Gueguen, L., Datcu, M.: Algorithmic information theory-based analysis of earth observation images: An assessment. IEEE Geoscience and Remote Sensing Letters 7(1), 8–12 (2010)
Vázquez, P., Marco, J.: Using normalized compression distance for image similarity measurement: an experimental study. The Visual Computer, 1–22 (2011)
Ghanbari, M.: Standard Codecs: Image Compression to Advanced Video Coding. The Institution of Electrical Engineers, London (2003)
Campana, B., Keogh, E.: A compression-based distance measure for texture. Statistical Analysis and Data Mining 3(6), 381–398 (2010)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2012 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Gangeh, M.J., Ghodsi, A., Kamel, M.S. (2012). Supervised Texture Classification Using a Novel Compression-Based Similarity Measure. In: Bolc, L., Tadeusiewicz, R., Chmielewski, L.J., Wojciechowski, K. (eds) Computer Vision and Graphics. ICCVG 2012. Lecture Notes in Computer Science, vol 7594. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-33564-8_46
Download citation
DOI: https://doi.org/10.1007/978-3-642-33564-8_46
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-33563-1
Online ISBN: 978-3-642-33564-8
eBook Packages: Computer ScienceComputer Science (R0)