Abstract
A content-based tile retrieval system based on the underlying multispectral Markov random field representation is introduced. Single tiles are represented by our approved textural features derived from especially efficient Markovian statistics and supplemented with Local Binary Patterns (LBP) features representing occasional tile inhomogeneities. Markovian features are on top of that also invariant to illumination colour and robust to illumination direction variations, therefore an arbitrary illuminated tiles do not negatively influence the retrieval result. The presented computer-aided tile consulting system retrieves tiles from recent tile production digital catalogues, so that the retrieved tiles have as similar pattern and/or colours to a query tile as possible. The system is verified on a large commercial tile database in a psychovisual experiment.
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Ahonen, T., Matas, J., He, C., Pietikäinen, M.: Rotation invariant image description with local binary pattern histogram fourier features. In: Salberg, A.B., Hardeberg, J.Y., Jenssen, R. (eds.) SCIA 2009. LNCS, vol. 5575, pp. 61–70. Springer, Heidelberg (2009)
Burghouts, G.J., Geusebroek, J.M.: Material-specific adaptation of color invariant features. Pattern Recognition Letters 30, 306–313 (2009)
Burghouts, G.J., Geusebroek, J.M.: Performance evaluation of local colour invariants. Comput. Vision and Image Understanding 113(1), 48–62 (2009)
Chen, Y., Wang, J.Z., Krovetz, R.: Clue: Cluster-based retrieval of images by unsupervised learning. IEEE Trans. Image Process. 14(8), 1187–1201 (2005)
Haindl, M., Å imberovĂ¡, S.: A Multispectral Image Line Reconstruction Method. In: Theory & Applications of Image Analysis, pp. 306–315. World Scientific Publishing Co., Singapore (1992)
Li, W., Wang, C., Wang, Q., Chen, G.: A generic system for the classification of marble tiles using gabor filters. In: ISCIS 2008, pp. 1–6 (2008)
Liao, S., Law, M.W.K., Chung, A.C.S.: Dominant local binary patterns for texture classification. IEEE Trans. Image Process. 18(5), 1107–1118 (2009)
Ma, W.Y., Manjunath, B.S.: Texture features and learning similarity, pp. 425–430. IEEE, Los Alamitos (1996)
Monadjemi, A.: Towards efficient texture classification and abnormality detection. Ph.D. thesis, University of Bristol (2004)
Ojala, T., Pietikäinen, M., Mäenpää, T.: Multiresolution gray-scale and rotation invariant texture classification with local binary patterns. IEEE Trans. Pattern Anal. Mach. Intell. 24(7), 971–987 (2002)
Santini, S., Jain, R.: Similarity measures. IEEE Trans. Pattern Anal. Mach. Intell. 21(9), 871–883 (1999)
Shotton, J.D.J., Winn, J., Rother, C., Criminisi, A.: Textonboost for image understanding: Multi-class object recognition and segmentation by jointly modeling texture, layout, and context. Int. J. Comput. Vision 81(1), 2–23 (2009)
Snoek, C.G.M., van de Sande, K.E.A., de Rooij, O., Huurnink, B., van Gemert, J., Uijlings, J.R.R., He, J., Li, X., Everts, I., Nedovic, V., van Liempt, M., van Balen, R., de Rijke, M., Geusebroek, J.M., Gevers, T., Worring, M., Smeulders, A.W.M., Koelma, D., Yan, F., Tahir, M.A., Mikolajczyk, K., Kittler, J.: The mediamill TRECVID 2008 semantic video search engine. In: Over, P., Awad, G., Rose, R.T., Fiscus, J.G., Kraaij, W., Smeaton, A.F. (eds.) TRECVID. National Institute of Standards and Technology, NIST (2008)
Stricker, M., Orengo, M.: Similarity of color images. In: Storage and retrieval for Image and Video Databases III, Ferbruary 1995. SPIE Proceeding Series, vol. 2420, pp. 381–392. SPIE, Bellingham (1995)
Vacha, P., Haindl, M.: Image retrieval measures based on illumination invariant textural MRF features. In: Sebe, N., Worring, M. (eds.) CIVR, pp. 448–454. ACM, New York (2007)
Vacha, P., Haindl, M.: Illumination invariants based on markov random fields. In: Proc. of the 19th International Conference on Pattern Recognition (2008)
Vacha, P., Haindl, M.: Natural material recognition with illumination invariant textural features. In: Proc. of the 20th International Conference on Pattern Recognition (2010) (accepted)
Varma, M., Zisserman, A.: A statistical approach to texture classification from single images. Int. J. Comput. Vision 62(1-2), 61–81 (2005)
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VĂ¡cha, P., Haindl, M. (2010). Content-Based Tile Retrieval System. In: Hancock, E.R., Wilson, R.C., Windeatt, T., Ulusoy, I., Escolano, F. (eds) Structural, Syntactic, and Statistical Pattern Recognition. SSPR /SPR 2010. Lecture Notes in Computer Science, vol 6218. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-14980-1_42
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DOI: https://doi.org/10.1007/978-3-642-14980-1_42
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