Abstract
Texture features play an important role in facilitating various applications, for instance, image retrieval and object recognition. In this work, we investigate the relational features as a texture descriptor in classifying materials and visual textures from their appearance. The relational features used in this paper are constructed by histogramming the values extracted for each point within an image with fuzzy histogram. To test the performance of relational features, two benchmarks were used which have a variety of poses and conditions. Despite the challenging occurrence in both benchmarks, impressive results were achieved by using the relational features.
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References
Burkhardt, H., Siggelkow, S.: Invariant Features in Pattern Recognition. In: Kotropoulos, C., Pitas, I. (eds.) Nonlinear Model-Based Image/Video Processing and Analysis, pp. 269–307. John Wisley and Sons (2001)
Burges, C.J.C.: A Tutorial on Support Vector Machines for Pattern Recognition. Data Mining and Knowledge Discovery 2, 121–167 (1998)
Haralick, R.M., Shanmugam, K., Dinstein, I.: Textural Features for Image Classification. IEEE Transactions on Systems, Man, and Cybernatics SMC 6, 610–621 (1973)
Hayman, E., Caputo, B., Fritz, M., Eklundh, J.-O.: On the Significance of Real-World Conditions for Material Classification. In: Pajdla, T., Matas, J(G.) (eds.) ECCV 2004. LNCS, vol. 3024, pp. 253–266. Springer, Heidelberg (2004)
Manjunath, B.S., Ma, W.Y.: Texture Features for Browsing and Retrieval of Image Data. IEEE Transaction on Pattern Analysis and Machine Intelligent 18(8), 837–842 (1996)
Ojala, T., Pietikäinen, M., Mäenpää, T.: Gray Scale and Rotation Invariant Texture Classification with Local Binary Patterns. In: Vernon, D. (ed.) ECCV 2000. LNCS, vol. 1842, pp. 404–420. Springer, Heidelberg (2000)
Schael, M.: Invariant grey scale features for texture analysis based on group averaging with relational kernel function, Internal Report 01/01, University of Freiburg (2001)
Siggelkow, S., Schael, M., Burkhardt, H.: SIMBA - Search IMages By Appearance. In: Radig, B., Florczyk, S. (eds.) DAGM 2001. LNCS, vol. 2191, pp. 9–16. Springer, Heidelberg (2001)
Setia, L., Teynor, A., Halawani, A., Burkhardt, H.: Radiograph annotation using local relational features. In: Workshop on Cross Language Evaluation Forum, CLEF (2006)
Tamura, H., Yamawaki, T.: Textural features corresponding to visual perception. IEEE Transaction on Systems, Man, and Cybernetics 8(6), 460–472 (1978)
MIT Media Lab Vision Textures, http://vismod.www.media.mit.edu
Fritz, M., Hayman, E., Caputo, B., Eklundh, J.-O.: The KTH-TIPS database, http://www.nada.kth.se/cvap/databases/kth-tips
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Hj Wan Yussof, W.N.J., Burkhardt, H. (2011). Relational Features for Texture Classification. In: Kim, Th., Adeli, H., Ramos, C., Kang, BH. (eds) Signal Processing, Image Processing and Pattern Recognition. SIP 2011. Communications in Computer and Information Science, vol 260. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-27183-0_47
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DOI: https://doi.org/10.1007/978-3-642-27183-0_47
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