Abstract
The performance of the K-views template (K-views-T) algorithm is related to the size of a view template and the number of characteristic views in the set of characteristic views. If the size of a view template and the number of characteristic views are increased, the classification accuracy will be improved at the expense of the time complexity. To reduce the time complexity of the K-views-T algorithm and maintain the high classification accuracy, the algorithm can utilize the datagram in which the frequencies of characteristic views are cumulated and distributed in a histogram. Due to the use of frequency, a smaller size of the view can be used for maintaining similar classification accuracy. In a sense, this is very similar to the approach used in the LBP and Textural Unit in which a histogram depicting the distribution of all the frequency (i.e., number) for a texture patch is used for the classification. In the basic K-views-T algorithm, the decision is made by a single characteristic view whose center is located at the current pixel being classified. By using this new datagram in the K-views model, the decision is made by the distribution of all the views contained in a large patch (i.e., block) in which the current pixel is the center of the block. Hence, a new K-views datagram algorithm is developed based on the datagram concept. Due to the spatial template used for the view, a boundary-refined method is described to improve the boundary pixel classification.
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— Lao Tzu
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Arasteh S, Hung C-C (2006) Color and texture image segmentation using uniform local binary pattern. Mach Vis Graph 15(3/4):265−274
Arasteh S, Hung C-C, Kuo B-C (2006) Image texture segmentation using local binary pattern and color information. In: The proceedings of the international computer symposium (ICS 2006), Taipei, Taiwan, 4−6 Dec 2006
Brodatz P (1966) Textures: a photographic album for artists and designers. Dover Publications, New York
He D-C, Wang L (1989) Texture unit, texture spectrum, and texture analysis. In: Proceedings of IGARSS’ 89/12th Canadian symposium on remote sensing, vol 5. pp 2769−2772
He D-C, Wang L (1990) Texture unit, texture spectrum, and texture analysis. In: IEEE transactions on geosciences and remote sensing, vol 28, issue 4
Hung C-C, Yang S, Laymon C (2002) Use of characteristic views in image classification. In: Proceedings of 16th international conference on pattern recognition, pp 949−952
Hung C-C, Pham M, Arasteh S, Kuo B-C Coleman T (2006) Image texture classification using texture spectrum and local binary pattern. In: The 2006 IEEE international geoscience & remote sensing symposium (IGARSS), Denver, Colorado, USA, 31 July−4 Aug 2006
Lan Y, Liu H, Song E, Hung C-C (2010) An improved K-view algorithm for image texture classification using new characteristic views selection methods. In: Proceedings of the 25th association of computing machinery (ACM) symposium on applied computing (SAC 2010)–Computational intelligence and image analysis (CIIA) track, Sierre, Swizerland, 21−26 March 2010, pp 960−964. https://doi.org/10.1145/1774088.1774288
Lan Y, Liu H, Song E, Hung C-C (2011) A comparative study and analysis on K-view based algorithms for image texture classification. In: Proceedings of the 26th association of computing machinery (ACM) symposium on applied computing (SAC 2011)−computational intelligence, signal and image analysis (CISIA) track, Taichung, Taiwan, 21−24 March 2011.https://doi.org/10.1145/1982185.1982372
Ojala T, Pietikainen M, Maenpaa T (2002) Multiresolution gray-scale and rotation invariant texture classification with local binary patterns. In: IEEE transaction on pattern recognition and machine intelligence, vol 24, issue 7
Song E, Jin MR, Hung C-C, Lu Y, Xu X (2007) Boundary refined texture segmentation based on K-Views and datagram method. In: Proceedings of the 2007 IEEE international symposium on computational intelligence in image and signal processing (CIISP 2007), Honolulu, HI, USA, 1−6 April 2007, pp 19−23
Wang L, He D-C (1990) A new statistical approach for texture analysis. Photogramm Eng Remote Sens 56(1):61–66
Yang S, Hung C-C (2003) Image texture classification using datagrams and characteristic views. In: Proceedings of the 18th ACM symposium on applied computing (SAC), Melbourne, FL, 9−12 March 2003, pp 22−26. https://doi.org/10.1145/952532.952538
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Hung, CC., Song, E., Lan, Y. (2019). Using Datagram in the K-views Model. In: Image Texture Analysis. Springer, Cham. https://doi.org/10.1007/978-3-030-13773-1_6
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DOI: https://doi.org/10.1007/978-3-030-13773-1_6
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