Abstract
In this paper some experimental results obtained by the application of various image quality assessment methods for the estimation of similarity of textile materials are presented. Such approach is considered as a part of an artificial intelligence based system developed for the recognition of clothing styles based on multi-dimensional analysis of descriptors and features.
For the verification of the usefulness of image quality metrics for this purpose, mainly those based on the comparison of the local similarity of image fragments have been chosen. Nevertheless, since the most of them are applied only for grayscale images, various methods of color to grayscale conversion have been analyzed. Obtained results are promising and may be successfully applied in combination with some other algorithms used e.g. in CBIR systems. Since the analyzed metrics do not use any information related to shape of objects, further combination with shape and color descriptors may be used.
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References
Aja-Fernandez, S., Estepar, R.S.J., Alberola-Lopez, C., Westiniu, C.F.: Image quality assessment based on local variance. In: Proc. 28th IEEE Int. Conf. Engineering in Medicine and Biology Society (EMBS), pp. 4815–4818 (2006)
Chen, G.H., Yang, C.L., Xie, S.L.: Gradient-based Structural Similarity for image quality assessment. In: Proc. IEEE Int. Conf. Image Processing (ICIP), pp. 2929–2932 (2006)
Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: IEEE Computer Society Conf. Computer Vision and Pattern Recognition (CVPR), vol. 1, pp. 886–893 (2005)
Gooch, A.A., Olsen, S.C., Tumblin, J., Gooch, B.B.: Color2gray: salience-preserving color removal. ACM Transactions on Graphics 24(3), 634–639 (2005)
Li, C., Bovik, A.: Three-component weighted structural similarity index. In: Proceedings of SPIE - Image Quality and System Performance VI, San Jose, California, vol. 72420, p. 72420Q (2009)
Li, C., Bovik, A.: Content-partitioned structural similarity index for image quality assessment. Signal Processing: Image Communication 25(7), 517–526 (2010)
Loke, K.S., Cheong, M.: Efficient textile recognition via decomposition of co-occurrence matrices. In: Proc. IEEE Int. Conf. Signal and Image Processing Applications (ICSIPA), pp. 257–261 (2009)
Ojala, T., Pietikäinen, M., Mäenpää, T.: Multiresolution gray-scale and rotation invariant texture classfication with local binary patterns. IEEE Trans. Pattern Anal. Machine Intell. 24(7), 971–987 (2002)
Okarma, K., Forczmański, P.: 2DLDA-based texture recognition in the aspect of objective image quality assessment. Annales UMCS - Informatica 8(1), 99–110 (2008)
Palm, C.: Color texture classification by integrative co-occurrence matrices. Pattern Recognition 37(5), 965–976 (2004)
Sampat, M., Wang, Z., Gupta, S., Bovik, A., Markey, M.: Complex wavelet structural similarity: A new image similarity index. IEEE Trans. Image Processing 18(11), 2385–2401 (2009)
Unser, M.: Texture classification and segmentation using wavelet frames. IEEE Trans. Image Processing 4(11), 1549–1560 (1995)
Žujović, J.: Perceptual Texture Similarity Metrics. PhD dissertation, Northwestern University, Evanston, Illinois (2011)
Žujović, J., Pappas, T.N., Neuhoff, D.L.: Structural similarity metrics for texture analysis and retrieval. In: Proc. 16 th IEEE Int. Conf. Image Processing ICIP, pp. 2225–2228 (2009)
Wang, Z., Bovik, A., Sheikh, H., Simoncelli, E.: Image quality assessment: From error measurement to Structural Similarity. IEEE Trans. Image Processing 13(4), 600–612 (2004)
Wang, Z., Li, Q.: Information content weighting for perceptual image quality assessment. IEEE Trans. Image Processing 20(5), 1185–1198 (2011)
Wang, Z., Simoncelli, E., Bovik, A.: Multi-Scale Structural Similarity for image quality assessment. In: Proc. 37th IEEE Asilomar Conf. Signals, Systems and Computers, Pacific Grove, California (2003)
Wu, J., Rehg, J.: CENTRIST: A visual descriptor for scene categorization. IEEE Trans. Pattern Anal. Machine Intell. 33(8), 1489–1501 (2011)
Zhang, L., Zhang, L., Mou, X., Zhang, D.: FSIM: A feature similarity index for image quality assessment. IEEE Trans. Image Processing 20(8), 2378–2386 (2011)
Zhang, L., Zhang, L., Mou, X.: RFSIM: A feature based image quality assessment metric using Riesz transforms. In: Proc. 17th IEEE Int. Conf. Image Processing (ICIP), pp. 321–324 (2010)
Zhao, X., Reyes, M.G., Pappas, T.N., Neuhoff, D.L.: Structural texture similarity metrics for retrieval applications. In: Proc. 15th IEEE Int. Conf. Image Processing (ICIP), pp. 1196–1199 (2008)
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Okarma, K., Frejlichowski, D., Czapiewski, P., Forczmański, P., Hofman, R. (2014). Similarity Estimation of Textile Materials Based on Image Quality Assessment Methods. In: Chmielewski, L.J., Kozera, R., Shin, BS., Wojciechowski, K. (eds) Computer Vision and Graphics. ICCVG 2014. Lecture Notes in Computer Science, vol 8671. Springer, Cham. https://doi.org/10.1007/978-3-319-11331-9_57
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DOI: https://doi.org/10.1007/978-3-319-11331-9_57
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