The system is designed to show images which are
related to the query image. Extracting color, texture, and shape
features from an image plays a vital role in content-based image
retrieval (CBIR). Initially RGB image is converted into HSV color
space due to its perceptual uniformity. From the HSV image, Color
features are extracted using block color histogram, texture features
using Haar transform and shape feature using Fuzzy C-means
Algorithm. Then, the characteristics of the global and local color
histogram, texture features through co-occurrence matrix and Haar
wavelet transform and shape are compared and analyzed for CBIR.
Finally, the best method of each feature is fused during similarity
measure to improve image retrieval effectiveness and accuracy.
[1] J. Almeida, A. Rocha, A, R. Torres and S. Goldenstein, "Making colors
worth more than a thousand words”, SAC’08., pp. 1180-1186, 2008.
[2] Y. Chan and C. Chen, "Image retrieval system based on colorcomplexity
and color-spatial features”, The Journal of Systems and
Software., vol. 71 pp. 65–70, 2004.
[3] R. Datta, D. Joshi, J.I.A. Li and J. Z Wang, "Image Retrieval: Ideas,
Influences and Trends of the New Age”, ACM Computing Surveys., vol.
40 pp. 1–60 , 2008.
[4] S Deb and Y Zhang, "An Overview of Content-based Image Retrieval
Techniques”, 18th International Conference on Advanced Information
Networking and Application (AINA’04), 2004.
[5] C. Hong, N. Li, M. Song, J. Bu and C. Chen, "Neurocomputing An
efficient approach to content-based object retrieval in videos”,
Neurocomputing., vol. 74, pp. 3565–3575 ,2011.
[6] W. Huang, Y. Gao and K. Luk, "A Review of Region-Based Image
Retrieval”, J Sign Process Syst ., vol. 59, pp. 143–161, 2010.
[7] B. M. Mehtre, S. Mohan, Kankanhalli and F. Wing, "Shape Measures
For Content Based Image Retrieval: A Comparison”, lnfmnatinn
Pmcessing & Management., vol. 33, pp. 319–337, 1997.
[8] P. Muneesawang and L. Guan, "An Interactive Approach for CBIR
Using a Network of Radial Basis Functions”, IEEE Transactions on
Multimedia., vol. 6, pp. 703–716, 2004.
[9] K. Sakthivel, T. Ravichandran and C. Kavitha, "Performance
Enhancement in Image Retrieval Using Weighted Dynamic Region
Matching”, European Journal of Scientific Research., vol. 56, no.3, pp.
385-395, 2011.
[10] T. K. Shih, L.Y. Deng, C. Wang and S. Yeh, "Content-based Image
Retrieval with Intensive Signature via Affine Invariant Transformation”,
IEEE ., pp. 393–400, 2000.
[11] X. Wang, Y. Yu and H. Yang, "Computer Standards & Interfaces An
effective image retrieval scheme using color, texture and shape
features”, Computer Standards & Interfaces., vol. 33, pp. 59–68, 2011.
[12] M. Zhang, Z. Lu and J. Shen, "Image Retrieval with Simple Invariant
Features Based Hierarchical Uniform Segmentation”, International
Conference on Computational Intelligence and Security., pp. 461–465,
2007.
[1] J. Almeida, A. Rocha, A, R. Torres and S. Goldenstein, "Making colors
worth more than a thousand words”, SAC’08., pp. 1180-1186, 2008.
[2] Y. Chan and C. Chen, "Image retrieval system based on colorcomplexity
and color-spatial features”, The Journal of Systems and
Software., vol. 71 pp. 65–70, 2004.
[3] R. Datta, D. Joshi, J.I.A. Li and J. Z Wang, "Image Retrieval: Ideas,
Influences and Trends of the New Age”, ACM Computing Surveys., vol.
40 pp. 1–60 , 2008.
[4] S Deb and Y Zhang, "An Overview of Content-based Image Retrieval
Techniques”, 18th International Conference on Advanced Information
Networking and Application (AINA’04), 2004.
[5] C. Hong, N. Li, M. Song, J. Bu and C. Chen, "Neurocomputing An
efficient approach to content-based object retrieval in videos”,
Neurocomputing., vol. 74, pp. 3565–3575 ,2011.
[6] W. Huang, Y. Gao and K. Luk, "A Review of Region-Based Image
Retrieval”, J Sign Process Syst ., vol. 59, pp. 143–161, 2010.
[7] B. M. Mehtre, S. Mohan, Kankanhalli and F. Wing, "Shape Measures
For Content Based Image Retrieval: A Comparison”, lnfmnatinn
Pmcessing & Management., vol. 33, pp. 319–337, 1997.
[8] P. Muneesawang and L. Guan, "An Interactive Approach for CBIR
Using a Network of Radial Basis Functions”, IEEE Transactions on
Multimedia., vol. 6, pp. 703–716, 2004.
[9] K. Sakthivel, T. Ravichandran and C. Kavitha, "Performance
Enhancement in Image Retrieval Using Weighted Dynamic Region
Matching”, European Journal of Scientific Research., vol. 56, no.3, pp.
385-395, 2011.
[10] T. K. Shih, L.Y. Deng, C. Wang and S. Yeh, "Content-based Image
Retrieval with Intensive Signature via Affine Invariant Transformation”,
IEEE ., pp. 393–400, 2000.
[11] X. Wang, Y. Yu and H. Yang, "Computer Standards & Interfaces An
effective image retrieval scheme using color, texture and shape
features”, Computer Standards & Interfaces., vol. 33, pp. 59–68, 2011.
[12] M. Zhang, Z. Lu and J. Shen, "Image Retrieval with Simple Invariant
Features Based Hierarchical Uniform Segmentation”, International
Conference on Computational Intelligence and Security., pp. 461–465,
2007.
@article{"International Journal of Information, Control and Computer Sciences:68151", author = "K. Sakthivel and R. Nallusamy and C. Kavitha", title = "Image Retrieval Using Fused Features", abstract = "The system is designed to show images which are
related to the query image. Extracting color, texture, and shape
features from an image plays a vital role in content-based image
retrieval (CBIR). Initially RGB image is converted into HSV color
space due to its perceptual uniformity. From the HSV image, Color
features are extracted using block color histogram, texture features
using Haar transform and shape feature using Fuzzy C-means
Algorithm. Then, the characteristics of the global and local color
histogram, texture features through co-occurrence matrix and Haar
wavelet transform and shape are compared and analyzed for CBIR.
Finally, the best method of each feature is fused during similarity
measure to improve image retrieval effectiveness and accuracy.
", keywords = "Color Histogram, Haar Wavelet Transform, Fuzzy
C-means, Co-occurrence matrix; Similarity measure.", volume = "8", number = "9", pages = "1656-5", }
