Opponent Color and Curvelet Transform Based Image Retrieval System Using Genetic Algorithm
In order to retrieve images efficiently from a large
database, a unique method integrating color and texture features
using genetic programming has been proposed. Opponent color
histogram which gives shadow, shade, and light intensity invariant
property is employed in the proposed framework for extracting color
features. For texture feature extraction, fast discrete curvelet
transform which captures more orientation information at different
scales is incorporated to represent curved like edges. The recent
scenario in the issues of image retrieval is to reduce the semantic gap
between user’s preference and low level features. To address this
concern, genetic algorithm combined with relevance feedback is
embedded to reduce semantic gap and retrieve user’s preference
images. Extensive and comparative experiments have been conducted
to evaluate proposed framework for content based image retrieval on
two databases, i.e., COIL-100 and Corel-1000. Experimental results
clearly show that the proposed system surpassed other existing
systems in terms of precision and recall. The proposed work achieves
highest performance with average precision of 88.2% on COIL-100
and 76.3% on Corel, the average recall of 69.9% on COIL and 76.3%
on Corel. Thus, the experimental results confirm that the proposed
content based image retrieval system architecture attains better
solution for image retrieval.
[1] C. Faloutsos, R. Barber, M. Flickner, J. Hafner, W. Niblack, D.
Petkovic, and W. quitz, “Efficient and effective querying by image
content”, Journal of Intelligence Information Systems”, vol.3 pp. 231-
262, 1994.
[2] A. Pentland, R. W. Picard, and S. Scaroff, “Photobook: Content based
manipulation for image databases”, International Journal of Computer
Vision”, vol.3 pp. 233-254, 1996.
[3] A. Gupta, and R. Jain, “Visual information Retrieval”, Commun. ACM,
vol.5 pp. 70-79, 1997.
[4] J. Z. Wang, J. Li, and G. Wiederhold, Simplicity: semantics sensitive
integrated matching for picture libraries. IEEE Transactions on Pattern
Analysis and Machine Intelligence, vol.9 pp. 947-963,2001.
[5] J. R. Smith, and S. F. Chang, “Automated image retrieval using color
and texture”, Columbia University. Technical Report, CU/CTR 1995.
[6] J. Han, and K. Ma, “Fuzzy color histogram and its use in color image
retrieval”, IEEE Transactions on Image Processing, vol.11 pp. 944-952,
2002.
[7] Guoping Qiu, Xia Feng, and Jianzhong Fang, “Compressing histogram
representations for, automatic color photo categorization”, Pattern
Recognition, vol.37 pp.2177-2193, 2004.
[8] A. Vadivel, Sural Shamik, and A. K. Majumdar, “An integrated color
and intensity co-occurrence matrix”, Pattern Recognition Letters, vol.28
pp. 974-983, 2007.
[9] P. Greg, Z. Ramin, and M. Justin, “Comparing images using color
coherence vectors”, Proceeding in ACM Multimedia, vol.96 pp. 65–73,
1996.
[10] W. Niblack, R. Barber, W. Equitz, M. Flickner, E. Glasman, D.
Petkovic, P. Yanker, C. Faloutsos, G. Taubin, “Querying images by
content, using color, texture, and shape”, SPIE Conf. Storage and
Retrieval for Image and Video Database, vol.1908 pp. 173-187, 1993.
[11] Ji-Quan Ma, “Content-Based Image Retrieval with HSV Color Space
and Texture Features”, International Conference on WEBISM, pp. 61-
63, 2009.
[12] X. Wan, C. C. Kuo, “Color distribution analysis and quantization for
image retrieval”, In SPIE Storage and Retrieval for Image and Video
Databases IV, pp. 9–16,1996.
[13] M. W. Ying, and Z. Hong Jiang, “Benchmarking of image feature for
content-based retrieval”, Proceedings in. Asilomar Conference on.
Signal, System Computing, 1998.
[14] Zhenhua, L. Wenhui, and L. Bo, “An Improving Technique of Color
Histogram in Segmentation based Image Retrieval” Proceedings in IEEE
fourth International Conference on Information Assurance Security, pp.
381-384, 2009.
[15] Zhiming Liu, and Cheng Jun Liu, “Fusion of the complementary
Discrete Cosine Features in the YIQ color space for face recognition”
Computer Vision and Image Understanding, vol. 111 pp. 249-262,2009.
[16] Y. Chen, and E. Dougherty, Grey-Scale Morphological Granulometric
Texture Classification. Opt. Eng, vol.8 pp. 2713-2722, 1994.
[17] Andrzej Materka and Michal Strzelecki. Texture Analysis Methods – A
Review. Technical University of Lodz, Institute of Electronics, COST
B11 report, Brussels 1998.
[18] I. Jeena Jacob, K. G. Srinivasagan, and K. Jeya Priya, Local oppugnant
texture pattern for image retrieval system. Pattern Recognition Letters,
vol.42 pp.72-78, 2014.
[19] D. Zhang, M. M. Islam, G. Lu, and I. J. Sumana. Rotation Invariant
Curvelet Features for Region Based Image Retrieval. International
Journal of Computer Vision, vol.2 pp.187-201, 2012.
[20] Fan-Hui Kong, and Harbin. Image retrieval using both color and texture.
Proceedings in IEEE International Conference on Machine Learning
Cybernatics, vol.4 pp. 2228 – 2232, 2009.
[21] Manimala Sinha, and K. Hemachandran. Content Based Image Retrieval
using color and texture. International Journal of Signal and Image
Processing, vol.3 pp. 39-56, 2012.
[22] M. Levine. Vision in Man and Machine. McGraw-Hill 1985.
[23] Jing Yi Tou, Yong Haur Tay, and Phooi Yee Lau. One dimensional
Grey-level Co-occurrence Matrices for texture classification, IEEE
International symposium on. Information Technology, pp.1-6, 2008.
[24] M. Subrahmanyan Q. M. Jonathan, Wu, R. P. Maheshwari, and R.
Balasubramanian. Modified color motif co-occurrence matrix for image
indexing and retrieval. Computer. Electrical. Engineering, pp. 762-774,
2013.
[25] Li S. Modeling Image Analysis Problems Using Markov Random Fields.
Handbook of Statistics, vol.20 pp.1-43.2000. [26] Li S. Markov Random Fields Models in Computer Vision. Proceedings
in. IEEE Conference on Computer Vision and Pattern Recognition, 866-
869, 1994.
[27] Mauricio Gomez, A. Renato, and A. Salinas, A New Technique for
Texture Classification Using Markov Random Fields. International
Journal Computer Communication & Control, vol.2 pp.41-51, 2006.
[28] Elhabiby M, Elsharkawy A, El-Sheimy N. Second Generation Curvelet
Transform Vs Wavelet Transforms and Canny Edge Detector for Edge
Detection from World View-2 data, Int. J. Comput Sci. & Eng Survey
(IJCSES) 2012; 3: 1-13.
[29] Miri MS, Mahloojifar A. Retinal image analysis using curvelet
transform and multi structure elements morphology by reconstruction.
IEEE Trans. Biomed. Eng 2011; 5: 1183–1192,
[30] Starck JL, Candes EJ, Donoho DL. The curvelet transform for image
denoising. IEEE Trans Image Proc 2002; 6: 670–684.
[31] Kokare M, Biswas PK, Chatterji BN, Rotation-Invariant Texture Image
Retrieval Using Rotated Complex Wavelet Filters. IEEE T Systems,
Man, and Cybernetics 2006; 6: 1273-1282.
[32] Zhe-Ming Lu, Su-Zhi Li, Hans Burkhardt. A content based image
retrieval scheme in JPEG compressed domain. Int. J. Innovative
Comput. Infor. Contr 2008; 2:831-839.
[33] Mojsilovic A, Rogowitz B. Capturing image semantics with low level
descriptors. Proc. Int. Conf. Image Process 2001; 18-21.
[34] Dengsheng Zhang, Guojun Lu, Wei-Ying M. A survey of content based
image retrieval with high level semantics Pattern Recogn 2007; 40: 262-
282.
[35] Chih-Chin, Ying-Chuan Chen. A User Oriented Image Retrieval System
Based on Interactive Genetic Algorithm. IEEE Trans Instrum. Meas
2011; 60: 3318-3325.
[36] Aun Irtaza M, Arfan Jaffer, Eisa Aleisa, Tae-Sun Choi. Embedding
neural network for semantic association in content based image retrieval.
Multimedia Tools and Appl 2014; 72: 1913-1931.
[37] Roland Kwitt, Peter Meerwald, Andreas Uhl. Efficient Texture Image
Retrieval Using Copulas in a Bayesian Framework. IEEE Trans Image
Process 2011; 20: 2063-2077.
[38] Ja-Hwung Su, Wei-Jun Huang, Philip S Yu, Vincent S Tseng. Efficient
Relevance feedback for Content Based Image Retrieval by mining User
Navigation Patterns. IEEE Trans Knowl. Data Eng 2011; 23: 360-372.
[39] Rodrigo, Ricardo, Marcos. Multimodal retrieval with relevance feedback
based on genetic programming. Multimedia Tools. Appl 2014; 69: 991-
1019.
[40] Julia Vogel, Bernt Schiele. Semantic Modeling of Natural Scenes for
Content Based Image Retrieval. Int. J. Comput. Vision 2007; 72: 1-20.
[41] Xiaofei He, Oliver King, Wei-Ying Ma, Mingjing Li, Hong-Jiang
Zhang. Learning a Semantic Space from User’s Relevance feedback for
image retrieval. IEEE Trans Circuits Syst. video Technol 2003; 13: 39-
48.
[42] Tao Dacheng, Tang Xiaoou, Li Xuelong, Wu Xindong. Asymmetric
bagging and random subspace for support vector machines-based
relevance feedback in image retrieval. IEEE T Pattern Anal. Machine
Intell 2006; 28: 1088-1099.
[43] Yixin Chen, James Wang Z, Robert Krovetz. CLUE: Cluster Based
Retrieval of Images by Unsupervised Learning. IEEE Trans Image
Process 2005; 14: 1187-1201.
[44] Man KF, Tang KS, Kwong S. Genetic Algorithms: Concepts and
Applications. IEEE Trans. In. Electron 1996; 43: 519-534.
[45] Aljahdali S, Ansari A, Hundewale N. Classification of image database
using SVM with Gabor Magnitude. Int. Conf. Multimedia Comput. Syst
2012; 126-132.
[46] Chang E, Tong S. SVMactive – support vector machine active learning
for image retrieval. Proc. ACM Int. Conf. Multimedia 2000; 107-118.
[47] Zhang L, Liu F, Zhang B. Support vector machine active learning for
image retrieval. Int. Conf. Image Process 2011; 7-10.
[48] Bilenko M, Basu S, Mooney RJ. Integrating Constraints and metric
learning in semi supervised clustering. Proc. 21st Int. Conf. Mach.
Learn. (ICML) 2004; 7: 172-179.
[49] Ying Liu, Dengsheng Zhang, Guojun Lu. Region Based Image retrieval
with high level semantics using decision tree learning. Pattern Recogn
2008; 41: 2554-2570.
[50] Ruofei Zhang, Zhongfei (Mark) Zhang. BALAS: Empirical Bayesian
learning in the relevance feedback for image retrieval. Image and Vision
Comput 2006; 24: 211-223.
[51] Peng-Yeng Yin, Shin-Huei Li. Content-based image retrieval using
association rule mining with soft relevance feedback. J. Visual
Commun. Image Repre 2006; 17: 1108-1125.
[52] Sanjay K. Saha, Amit K. Das, Bhabatosh Chanda. Image retrieval based
on indexing and relevance feedback. pattern recogn. Lett 2011; 28: 357-
366.
[53] Koen EA. Van de Sande, Theo Gervers, Snoek CCM. Evaluating Color
Descriptors for Object and Scene Recognition. IEEE Trans. Pattern
Anal. Mach. Intell 2010; 32: 1582-1596.
[54] Martinkauppa JB, Piettikäinen M. Facial Skin Color Modeling.
Handbook of face Recogn. Springer Science & Business 2005; 113-135.
[55] Jones MJ, Rehg JM. Statistical color models with application to skin
detection Proc CVPR ’99 1999; 1: 274–280.
[56] Emmanuel Cand`es, Laurent Demanet, David Donoho, Lexing Ying.
Fast Discrete Curvelet Transforms. Appl. Comput. Math 2006; 7: 1-44
[1] C. Faloutsos, R. Barber, M. Flickner, J. Hafner, W. Niblack, D.
Petkovic, and W. quitz, “Efficient and effective querying by image
content”, Journal of Intelligence Information Systems”, vol.3 pp. 231-
262, 1994.
[2] A. Pentland, R. W. Picard, and S. Scaroff, “Photobook: Content based
manipulation for image databases”, International Journal of Computer
Vision”, vol.3 pp. 233-254, 1996.
[3] A. Gupta, and R. Jain, “Visual information Retrieval”, Commun. ACM,
vol.5 pp. 70-79, 1997.
[4] J. Z. Wang, J. Li, and G. Wiederhold, Simplicity: semantics sensitive
integrated matching for picture libraries. IEEE Transactions on Pattern
Analysis and Machine Intelligence, vol.9 pp. 947-963,2001.
[5] J. R. Smith, and S. F. Chang, “Automated image retrieval using color
and texture”, Columbia University. Technical Report, CU/CTR 1995.
[6] J. Han, and K. Ma, “Fuzzy color histogram and its use in color image
retrieval”, IEEE Transactions on Image Processing, vol.11 pp. 944-952,
2002.
[7] Guoping Qiu, Xia Feng, and Jianzhong Fang, “Compressing histogram
representations for, automatic color photo categorization”, Pattern
Recognition, vol.37 pp.2177-2193, 2004.
[8] A. Vadivel, Sural Shamik, and A. K. Majumdar, “An integrated color
and intensity co-occurrence matrix”, Pattern Recognition Letters, vol.28
pp. 974-983, 2007.
[9] P. Greg, Z. Ramin, and M. Justin, “Comparing images using color
coherence vectors”, Proceeding in ACM Multimedia, vol.96 pp. 65–73,
1996.
[10] W. Niblack, R. Barber, W. Equitz, M. Flickner, E. Glasman, D.
Petkovic, P. Yanker, C. Faloutsos, G. Taubin, “Querying images by
content, using color, texture, and shape”, SPIE Conf. Storage and
Retrieval for Image and Video Database, vol.1908 pp. 173-187, 1993.
[11] Ji-Quan Ma, “Content-Based Image Retrieval with HSV Color Space
and Texture Features”, International Conference on WEBISM, pp. 61-
63, 2009.
[12] X. Wan, C. C. Kuo, “Color distribution analysis and quantization for
image retrieval”, In SPIE Storage and Retrieval for Image and Video
Databases IV, pp. 9–16,1996.
[13] M. W. Ying, and Z. Hong Jiang, “Benchmarking of image feature for
content-based retrieval”, Proceedings in. Asilomar Conference on.
Signal, System Computing, 1998.
[14] Zhenhua, L. Wenhui, and L. Bo, “An Improving Technique of Color
Histogram in Segmentation based Image Retrieval” Proceedings in IEEE
fourth International Conference on Information Assurance Security, pp.
381-384, 2009.
[15] Zhiming Liu, and Cheng Jun Liu, “Fusion of the complementary
Discrete Cosine Features in the YIQ color space for face recognition”
Computer Vision and Image Understanding, vol. 111 pp. 249-262,2009.
[16] Y. Chen, and E. Dougherty, Grey-Scale Morphological Granulometric
Texture Classification. Opt. Eng, vol.8 pp. 2713-2722, 1994.
[17] Andrzej Materka and Michal Strzelecki. Texture Analysis Methods – A
Review. Technical University of Lodz, Institute of Electronics, COST
B11 report, Brussels 1998.
[18] I. Jeena Jacob, K. G. Srinivasagan, and K. Jeya Priya, Local oppugnant
texture pattern for image retrieval system. Pattern Recognition Letters,
vol.42 pp.72-78, 2014.
[19] D. Zhang, M. M. Islam, G. Lu, and I. J. Sumana. Rotation Invariant
Curvelet Features for Region Based Image Retrieval. International
Journal of Computer Vision, vol.2 pp.187-201, 2012.
[20] Fan-Hui Kong, and Harbin. Image retrieval using both color and texture.
Proceedings in IEEE International Conference on Machine Learning
Cybernatics, vol.4 pp. 2228 – 2232, 2009.
[21] Manimala Sinha, and K. Hemachandran. Content Based Image Retrieval
using color and texture. International Journal of Signal and Image
Processing, vol.3 pp. 39-56, 2012.
[22] M. Levine. Vision in Man and Machine. McGraw-Hill 1985.
[23] Jing Yi Tou, Yong Haur Tay, and Phooi Yee Lau. One dimensional
Grey-level Co-occurrence Matrices for texture classification, IEEE
International symposium on. Information Technology, pp.1-6, 2008.
[24] M. Subrahmanyan Q. M. Jonathan, Wu, R. P. Maheshwari, and R.
Balasubramanian. Modified color motif co-occurrence matrix for image
indexing and retrieval. Computer. Electrical. Engineering, pp. 762-774,
2013.
[25] Li S. Modeling Image Analysis Problems Using Markov Random Fields.
Handbook of Statistics, vol.20 pp.1-43.2000. [26] Li S. Markov Random Fields Models in Computer Vision. Proceedings
in. IEEE Conference on Computer Vision and Pattern Recognition, 866-
869, 1994.
[27] Mauricio Gomez, A. Renato, and A. Salinas, A New Technique for
Texture Classification Using Markov Random Fields. International
Journal Computer Communication & Control, vol.2 pp.41-51, 2006.
[28] Elhabiby M, Elsharkawy A, El-Sheimy N. Second Generation Curvelet
Transform Vs Wavelet Transforms and Canny Edge Detector for Edge
Detection from World View-2 data, Int. J. Comput Sci. & Eng Survey
(IJCSES) 2012; 3: 1-13.
[29] Miri MS, Mahloojifar A. Retinal image analysis using curvelet
transform and multi structure elements morphology by reconstruction.
IEEE Trans. Biomed. Eng 2011; 5: 1183–1192,
[30] Starck JL, Candes EJ, Donoho DL. The curvelet transform for image
denoising. IEEE Trans Image Proc 2002; 6: 670–684.
[31] Kokare M, Biswas PK, Chatterji BN, Rotation-Invariant Texture Image
Retrieval Using Rotated Complex Wavelet Filters. IEEE T Systems,
Man, and Cybernetics 2006; 6: 1273-1282.
[32] Zhe-Ming Lu, Su-Zhi Li, Hans Burkhardt. A content based image
retrieval scheme in JPEG compressed domain. Int. J. Innovative
Comput. Infor. Contr 2008; 2:831-839.
[33] Mojsilovic A, Rogowitz B. Capturing image semantics with low level
descriptors. Proc. Int. Conf. Image Process 2001; 18-21.
[34] Dengsheng Zhang, Guojun Lu, Wei-Ying M. A survey of content based
image retrieval with high level semantics Pattern Recogn 2007; 40: 262-
282.
[35] Chih-Chin, Ying-Chuan Chen. A User Oriented Image Retrieval System
Based on Interactive Genetic Algorithm. IEEE Trans Instrum. Meas
2011; 60: 3318-3325.
[36] Aun Irtaza M, Arfan Jaffer, Eisa Aleisa, Tae-Sun Choi. Embedding
neural network for semantic association in content based image retrieval.
Multimedia Tools and Appl 2014; 72: 1913-1931.
[37] Roland Kwitt, Peter Meerwald, Andreas Uhl. Efficient Texture Image
Retrieval Using Copulas in a Bayesian Framework. IEEE Trans Image
Process 2011; 20: 2063-2077.
[38] Ja-Hwung Su, Wei-Jun Huang, Philip S Yu, Vincent S Tseng. Efficient
Relevance feedback for Content Based Image Retrieval by mining User
Navigation Patterns. IEEE Trans Knowl. Data Eng 2011; 23: 360-372.
[39] Rodrigo, Ricardo, Marcos. Multimodal retrieval with relevance feedback
based on genetic programming. Multimedia Tools. Appl 2014; 69: 991-
1019.
[40] Julia Vogel, Bernt Schiele. Semantic Modeling of Natural Scenes for
Content Based Image Retrieval. Int. J. Comput. Vision 2007; 72: 1-20.
[41] Xiaofei He, Oliver King, Wei-Ying Ma, Mingjing Li, Hong-Jiang
Zhang. Learning a Semantic Space from User’s Relevance feedback for
image retrieval. IEEE Trans Circuits Syst. video Technol 2003; 13: 39-
48.
[42] Tao Dacheng, Tang Xiaoou, Li Xuelong, Wu Xindong. Asymmetric
bagging and random subspace for support vector machines-based
relevance feedback in image retrieval. IEEE T Pattern Anal. Machine
Intell 2006; 28: 1088-1099.
[43] Yixin Chen, James Wang Z, Robert Krovetz. CLUE: Cluster Based
Retrieval of Images by Unsupervised Learning. IEEE Trans Image
Process 2005; 14: 1187-1201.
[44] Man KF, Tang KS, Kwong S. Genetic Algorithms: Concepts and
Applications. IEEE Trans. In. Electron 1996; 43: 519-534.
[45] Aljahdali S, Ansari A, Hundewale N. Classification of image database
using SVM with Gabor Magnitude. Int. Conf. Multimedia Comput. Syst
2012; 126-132.
[46] Chang E, Tong S. SVMactive – support vector machine active learning
for image retrieval. Proc. ACM Int. Conf. Multimedia 2000; 107-118.
[47] Zhang L, Liu F, Zhang B. Support vector machine active learning for
image retrieval. Int. Conf. Image Process 2011; 7-10.
[48] Bilenko M, Basu S, Mooney RJ. Integrating Constraints and metric
learning in semi supervised clustering. Proc. 21st Int. Conf. Mach.
Learn. (ICML) 2004; 7: 172-179.
[49] Ying Liu, Dengsheng Zhang, Guojun Lu. Region Based Image retrieval
with high level semantics using decision tree learning. Pattern Recogn
2008; 41: 2554-2570.
[50] Ruofei Zhang, Zhongfei (Mark) Zhang. BALAS: Empirical Bayesian
learning in the relevance feedback for image retrieval. Image and Vision
Comput 2006; 24: 211-223.
[51] Peng-Yeng Yin, Shin-Huei Li. Content-based image retrieval using
association rule mining with soft relevance feedback. J. Visual
Commun. Image Repre 2006; 17: 1108-1125.
[52] Sanjay K. Saha, Amit K. Das, Bhabatosh Chanda. Image retrieval based
on indexing and relevance feedback. pattern recogn. Lett 2011; 28: 357-
366.
[53] Koen EA. Van de Sande, Theo Gervers, Snoek CCM. Evaluating Color
Descriptors for Object and Scene Recognition. IEEE Trans. Pattern
Anal. Mach. Intell 2010; 32: 1582-1596.
[54] Martinkauppa JB, Piettikäinen M. Facial Skin Color Modeling.
Handbook of face Recogn. Springer Science & Business 2005; 113-135.
[55] Jones MJ, Rehg JM. Statistical color models with application to skin
detection Proc CVPR ’99 1999; 1: 274–280.
[56] Emmanuel Cand`es, Laurent Demanet, David Donoho, Lexing Ying.
Fast Discrete Curvelet Transforms. Appl. Comput. Math 2006; 7: 1-44
@article{"International Journal of Information, Control and Computer Sciences:71799", author = "Yesubai Rubavathi Charles and Ravi Ramraj", title = "Opponent Color and Curvelet Transform Based Image Retrieval System Using Genetic Algorithm", abstract = "In order to retrieve images efficiently from a large
database, a unique method integrating color and texture features
using genetic programming has been proposed. Opponent color
histogram which gives shadow, shade, and light intensity invariant
property is employed in the proposed framework for extracting color
features. For texture feature extraction, fast discrete curvelet
transform which captures more orientation information at different
scales is incorporated to represent curved like edges. The recent
scenario in the issues of image retrieval is to reduce the semantic gap
between user’s preference and low level features. To address this
concern, genetic algorithm combined with relevance feedback is
embedded to reduce semantic gap and retrieve user’s preference
images. Extensive and comparative experiments have been conducted
to evaluate proposed framework for content based image retrieval on
two databases, i.e., COIL-100 and Corel-1000. Experimental results
clearly show that the proposed system surpassed other existing
systems in terms of precision and recall. The proposed work achieves
highest performance with average precision of 88.2% on COIL-100
and 76.3% on Corel, the average recall of 69.9% on COIL and 76.3%
on Corel. Thus, the experimental results confirm that the proposed
content based image retrieval system architecture attains better
solution for image retrieval.", keywords = "Content based image retrieval, Curvelet transform,
Genetic algorithm, Opponent color histogram, Relevance feedback.", volume = "9", number = "9", pages = "2119-8", }
