Enhanced Multi-Intensity Analysis in Multi-Scenery Classification-Based Macro and Micro Elements
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.
[1] 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.
[2] 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.
[3] T. Menpa et al., “Real-time surface inspection by texture,” Real Time
Imaging, vol. 9, no. 5, pp. 289–296, Oct. 2003.
[4] 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.
[5] 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.
[6] 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.
[7] 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.
[8] Q. Song, “A new multi-scale texture analysis with structural texel,”,
CSIE 2011, Part I, CCIS 152, pp. 61–66, 2011.
[9] 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.
[10] 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.
[11] Q. Tian et al., “Display optimization for image browsing,”, MDIC 2001,
LNCS 2184, pp. 167–176, 2001.
[12] 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.
[13] 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.
[14] 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.
[15] 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.
[16] 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.
[17] D. Hearn and M. P. Baker, Computer Graphics C Version, 2nd ed. Book,
Prentice Hall, 1997.
[18] 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.
[19] 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/.
[20] R. Bremanath, “A study on transformation invariant pattern recognition,”
Ph.D. dissertation, Comp. Sci. Engg. Dept., Anna Univ., Chennai,
Madras, 2006.
[1] 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.
[2] 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.
[3] T. Menpa et al., “Real-time surface inspection by texture,” Real Time
Imaging, vol. 9, no. 5, pp. 289–296, Oct. 2003.
[4] 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.
[5] 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.
[6] 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.
[7] 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.
[8] Q. Song, “A new multi-scale texture analysis with structural texel,”,
CSIE 2011, Part I, CCIS 152, pp. 61–66, 2011.
[9] 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.
[10] 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.
[11] Q. Tian et al., “Display optimization for image browsing,”, MDIC 2001,
LNCS 2184, pp. 167–176, 2001.
[12] 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.
[13] 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.
[14] 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.
[15] 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.
[16] 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.
[17] D. Hearn and M. P. Baker, Computer Graphics C Version, 2nd ed. Book,
Prentice Hall, 1997.
[18] 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.
[19] 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/.
[20] R. Bremanath, “A study on transformation invariant pattern recognition,”
Ph.D. dissertation, Comp. Sci. Engg. Dept., Anna Univ., Chennai,
Madras, 2006.
@article{"International Journal of Information, Control and Computer Sciences:75620", author = "R. Bremananth", title = "Enhanced Multi-Intensity Analysis in Multi-Scenery Classification-Based Macro and Micro Elements", 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.", keywords = "Automatic classification, contrast, homogeneity,
invariant analysis, multi-scene analysis, overlapping.", volume = "11", number = "4", pages = "485-8", }
