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Paper Count: 31533
Enhanced Multi-Intensity Analysis in Multi-Scenery Classification-Based Macro and Micro Elements
Authors: R. Bremananth
Abstract:Several computationally challenging issues are encountered while classifying complex natural scenes. In this paper, we address the problems that are encountered in rotation invariance with multi-intensity analysis for multi-scene overlapping. In the present literature, various algorithms proposed techniques for multi-intensity analysis, but there are several restrictions in these algorithms while deploying them in multi-scene overlapping classifications. In order to resolve the problem of multi-scenery overlapping classifications, we present a framework that is based on macro and micro basis functions. This algorithm conquers the minimum classification false alarm while pigeonholing multi-scene overlapping. Furthermore, a quadrangle multi-intensity decay is invoked. Several parameters are utilized to analyze invariance for multi-scenery classifications such as rotation, classification, correlation, contrast, homogeneity, and energy. Benchmark datasets were collected for complex natural scenes and experimented for the framework. The results depict that the framework achieves a significant improvement on gray-level matrix of co-occurrence features for overlapping in diverse degree of orientations while pigeonholing multi-scene overlapping.
Digital Object Identifier (DOI): doi.org/10.5281/zenodo.1130603Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 586
 R. Bremananth, “Multi-intensity analysis for overlapping of invariant textures in mobile communications,” Inte. Conf. on Advances in Mobile Network, Communication and its Applications, 2012. IEEE: 978-0-7695-4720-6/12. doi:10.1109/MNCApps.2012.10.
 D. Wal et al., ”Characterisation of surface roughnessand sediment texture of intertidal flats using ERS SAR imagery,” Remote Sens. Environ., vol. 98, no. 1, pp. 96–109, Sep. 2005.
 T. Menpa et al., “Real-time surface inspection by texture,” Real Time Imaging, vol. 9, no. 5, pp. 289–296, Oct. 2003.
 W. M. Chen et al., “3-D ultrasound texture classification using run difference matrix,” Ultrasound in Med. & Biol., vol. 31, no. 6, pp. 763–770, Jun. 2005.
 B. Verma and S. Kulkarni, “A fuzzy-neural approach for interpretation and fusion of colour and texture features for CBIR systems,” Appl. Soft Comput., vol. 5, no. 1, pp. 119–130, Dec. 2004.
 T. Ojala et al., “Multiresolution gray-scale and rotation invariant texture classification with local binary patterns,” IEEE Trans. Pattern Anal. Machine Intell., vol. 24, no. 7, pp.971–987, Jul. 2002.
 J. L. Chen, “Rotation and gray scale transform invariant texture identification using wavelet decomposition and hidden markov model”, IEEE Trans. Pattern Anal. Machine Intell., vol. 16, no. 2, pp. 208–214, Feb. 1994.
 Q. Song, “A new multi-scale texture analysis with structural texel,”, CSIE 2011, Part I, CCIS 152, pp. 61–66, 2011.
 J. R. Smith and S. F. Chang, “Automated binary texture feature image retrieval,” Proc. IEEE Intl. Conf. Acoust., Speech, and Signal Proc., Atlanta, GA, 1996. IEEE:1520-6149. doi:10.1109.
 J. R. Smith and S. F. Chang, “Transform features for texture classification and discrimination on large image database,” Proc. IEEE Intl. Conf. on Image Proc., 1994. IEEE:1520-6149. doi:10.1109/ICIP.1994.413817.
 Q. Tian et al., “Display optimization for image browsing,”, MDIC 2001, LNCS 2184, pp. 167–176, 2001.
 X. S. Zhou et al., “Water-filling algorithm: A novel way for image feature extraction based on edge maps,” Proc. IEEE Intl. Conf. On Image Proc., Japan, 1999. IEEE. doi:10.1109/ICIP.1999.822959.
 G. N. Srinivasan, and G. Shobha, “Statistical texture analysis,” Proc. of World Academy of Science, Engg. and Tech., vol.36, pp. 1264–1269, Dec. 2008.
 R. Bremananth et al., “Wood species recognition system,” Int. J. of Comp., Elec., Auto., Cont. and Inf. Eng., vol. 3, no. 4, pp. 1138-1144, 2009.
 R. Bremananth et al., “On-line rotation invariant estimation and recognition,” Inte. J. on Advanced Computer Science and Applications, vol. 1, no.2, pp.41–50, 2010.
 R. Bremananth et al., “Wood species recognition using GLCM and correlation,” Proc.of IEEE Computer society, Int. Conf. on Advances in Recent Technologies in Communication and Computing, 2009, pp. 615–619. IEEE: 978-0-7695-3845-7/09. doi:10.1109/ARTCom.2009.10.
 D. Hearn and M. P. Baker, Computer Graphics C Version, 2nd ed. Book, Prentice Hall, 1997.
 R. Bremananth et al., “An efficient superposition of acoustic field reconstruction in NAH,” 6th Int. Conf. on Information and Communication Systems, 2015, pp. 245250. IEEE. doi:10.1109/IACS.2015.7103183.
 R. Bremananth and H. K. H. Abujalban, “A robust framework for enhanced multi-intensity analysis in multi-scenery classification,” IEEE Jordan Conference on Applied Electrical Engineering and Computing Technologies, Jordan 2015, IEEE: 2015 978-1-4799-7431-3/15/.
 R. Bremanath, “A study on transformation invariant pattern recognition,” Ph.D. dissertation, Comp. Sci. Engg. Dept., Anna Univ., Chennai, Madras, 2006.