Target Detection with Improved Image Texture Feature Coding Method and Support Vector Machine
An image texture analysis and target recognition approach of using an improved image texture feature coding method (TFCM) and Support Vector Machine (SVM) for target detection is presented. With our proposed target detection framework, targets of interest can be detected accurately. Cascade-Sliding-Window technique was also developed for automated target localization. Application to mammogram showed that over 88% of normal mammograms and 80% of abnormal mammograms can be correctly identified. The approach was also successfully applied to Synthetic Aperture Radar (SAR) and Ground Penetrating Radar (GPR) images for target detection.
[1] S. Beag and N. Kehtarnavaz, "Texture based Classification of Mass
Abnormalities in Mammograms," The 13th IEEE Symposium on
Computer-Based Medical Systems (CBMS 2000), pp.163-168, 2000.
[2] R. M. Haralick, ÔÇÿÔÇÿStatistical and structural approaches to texture,--
Proc. IEEE 67, 786-804, 1979.
[3] L. Van Gool, P. Dewaele, and A. Oosterlinck, ÔÇÿÔÇÿSURVEY: Texture
analysis anno 1983,-- Comput. Vis. Graph. Image Process. 29,
pp.336-357, 1985i
[4] R. Gonzalez and P. Wintz, Digital Image Processing, 2th edition,
Addison Wesley, 1998i
[5] M. N. Shirazi, H. Noda, and N. Takao, ÔÇÿÔÇÿTexture classification based
on Markov modeling in wavelet features space,-- Image Vis. Comput.
18, pp.967-973, 2000.
[6] A. Ai-Janobi, ÔÇÿÔÇÿPerformance evaluation of cross-diagonal texture
matrix method of texture analysis,-- Pattern Recognition, 34, pp.171-
180, 2001.
[7] J. G. Leu, ÔÇÿÔÇÿOn indexing the periodicity of image textures,-- Image
Vis. Comput. 19, pp.987-1000, 2001.
[8] S. Baheerathan, F. Albregstsen, and H. E. Danielsen, ÔÇÿÔÇÿNew texture
features based on the complexity curve,-- Pattern Recognition, 32,
pp.605- 618, 1999.
[9] D. A. Clausi and M. E. Jernigan, ÔÇÿÔÇÿDesigning Gabor filters for optimal
texture separability,-- Pattern Recognition, 33, pp.1835-1849, 2000.
[10] A. M. Pun and M. C. Lee, ÔÇÿÔÇÿRotation-invariant texture classification
using a two-stage wavelet packet features approach,-- IEE Proc.
Vision Image Signal Process. 148, pp.422-428, 2001.
[11] M. H. Horng, Y. -N. Sun, and X. -Z. Lin, "Texture Feature Coding
Method for Classification of Liver Sonography", the 4th European
Conference on Computer Vision (ECCV96), Lecture Notes in
Computer Science 1064, pp.209-218, 1996.
[12] M. H. Horng, "Texture feature coding method for texture
classification," Opt. Eng., Vol 42 (1), pp. 228-238, 2003.
[13] V. Vapnik, Statistical Learning Theory. New York: Wiley 1998.
[14] R.M. Harlaick, K. Shanmugam, Itshak Dinstein, "Textural features for
image classification," IEEE Trans. System, Man and Cybernetics, vol.
3, pp. 610-621, 1973.
[15] L. Wang and D. C. He, ÔÇÿTexture classification using texture
spectrum," Pattern Recognition, vol. 23, pp.905-910, 1990.
[16] J. R. Carr and F. P. de Miranda, "The semivariogram in comparison to
the co-occurrence matrix for classification of image texture," IEEE
Trans. Geoscience and Remote Sensing, Vol. 36 (6), pp. 1945-1952,
No. 1998.
[17] Rober Burbidge, Bernard Buxton, "An introduction to Support Vector
Machines for data mining," Keynote papers, young OR 12, University
of Nottingham, M. Sheppee (ed), Operational research Society, pp.3-
15, 2001.
[18] O. Duda, E. Hart, and G. Stork, Pattern Recognition, John Wiley &
Sons, 2001.
[1] S. Beag and N. Kehtarnavaz, "Texture based Classification of Mass
Abnormalities in Mammograms," The 13th IEEE Symposium on
Computer-Based Medical Systems (CBMS 2000), pp.163-168, 2000.
[2] R. M. Haralick, ÔÇÿÔÇÿStatistical and structural approaches to texture,--
Proc. IEEE 67, 786-804, 1979.
[3] L. Van Gool, P. Dewaele, and A. Oosterlinck, ÔÇÿÔÇÿSURVEY: Texture
analysis anno 1983,-- Comput. Vis. Graph. Image Process. 29,
pp.336-357, 1985i
[4] R. Gonzalez and P. Wintz, Digital Image Processing, 2th edition,
Addison Wesley, 1998i
[5] M. N. Shirazi, H. Noda, and N. Takao, ÔÇÿÔÇÿTexture classification based
on Markov modeling in wavelet features space,-- Image Vis. Comput.
18, pp.967-973, 2000.
[6] A. Ai-Janobi, ÔÇÿÔÇÿPerformance evaluation of cross-diagonal texture
matrix method of texture analysis,-- Pattern Recognition, 34, pp.171-
180, 2001.
[7] J. G. Leu, ÔÇÿÔÇÿOn indexing the periodicity of image textures,-- Image
Vis. Comput. 19, pp.987-1000, 2001.
[8] S. Baheerathan, F. Albregstsen, and H. E. Danielsen, ÔÇÿÔÇÿNew texture
features based on the complexity curve,-- Pattern Recognition, 32,
pp.605- 618, 1999.
[9] D. A. Clausi and M. E. Jernigan, ÔÇÿÔÇÿDesigning Gabor filters for optimal
texture separability,-- Pattern Recognition, 33, pp.1835-1849, 2000.
[10] A. M. Pun and M. C. Lee, ÔÇÿÔÇÿRotation-invariant texture classification
using a two-stage wavelet packet features approach,-- IEE Proc.
Vision Image Signal Process. 148, pp.422-428, 2001.
[11] M. H. Horng, Y. -N. Sun, and X. -Z. Lin, "Texture Feature Coding
Method for Classification of Liver Sonography", the 4th European
Conference on Computer Vision (ECCV96), Lecture Notes in
Computer Science 1064, pp.209-218, 1996.
[12] M. H. Horng, "Texture feature coding method for texture
classification," Opt. Eng., Vol 42 (1), pp. 228-238, 2003.
[13] V. Vapnik, Statistical Learning Theory. New York: Wiley 1998.
[14] R.M. Harlaick, K. Shanmugam, Itshak Dinstein, "Textural features for
image classification," IEEE Trans. System, Man and Cybernetics, vol.
3, pp. 610-621, 1973.
[15] L. Wang and D. C. He, ÔÇÿTexture classification using texture
spectrum," Pattern Recognition, vol. 23, pp.905-910, 1990.
[16] J. R. Carr and F. P. de Miranda, "The semivariogram in comparison to
the co-occurrence matrix for classification of image texture," IEEE
Trans. Geoscience and Remote Sensing, Vol. 36 (6), pp. 1945-1952,
No. 1998.
[17] Rober Burbidge, Bernard Buxton, "An introduction to Support Vector
Machines for data mining," Keynote papers, young OR 12, University
of Nottingham, M. Sheppee (ed), Operational research Society, pp.3-
15, 2001.
[18] O. Duda, E. Hart, and G. Stork, Pattern Recognition, John Wiley &
Sons, 2001.
@article{"International Journal of Information, Control and Computer Sciences:55338", author = "R. Xu and X. Zhao and X. Li and C. Kwan and C.-I Chang", title = "Target Detection with Improved Image Texture Feature Coding Method and Support Vector Machine", abstract = "An image texture analysis and target recognition approach of using an improved image texture feature coding method (TFCM) and Support Vector Machine (SVM) for target detection is presented. With our proposed target detection framework, targets of interest can be detected accurately. Cascade-Sliding-Window technique was also developed for automated target localization. Application to mammogram showed that over 88% of normal mammograms and 80% of abnormal mammograms can be correctly identified. The approach was also successfully applied to Synthetic Aperture Radar (SAR) and Ground Penetrating Radar (GPR) images for target detection.
", keywords = "Image texture analysis, feature extraction, target detection, pattern classification.", volume = "2", number = "3", pages = "772-10", }
