Content-Based Color Image Retrieval Based On 2-D Histogram and Statistical Moments
In this paper, we are interested in the problem of
finding similar images in a large database. For this purpose we
propose a new algorithm based on a combination of the 2-D
histogram intersection in the HSV space and statistical moments. The
proposed histogram is based on a 3x3 window and not only on the
intensity of the pixel. This approach overcome the drawback of the
conventional 1-D histogram which is ignoring the spatial distribution
of pixels in the image, while the statistical moments are used to
escape the effects of the discretisation of the color space which is
intrinsic to the use of histograms. We compare the performance of
our new algorithm to various methods of the state of the art and we
show that it has several advantages. It is fast, consumes little memory
and requires no learning. To validate our results, we apply this
algorithm to search for similar images in different image databases.
[1] Niblack, W., Barber, R., Equitz, W., Flickner, M., Glasman, E.,
Petkovic, D., Yanker, P., Faloutsos, C., & Taubin, G. “The QBIC
Project: Querying Images by Content Using 37 Color, Texture, and
Shape”. SPIE Int. Symp. On Electronic Imaging: Science and
Technology Conf. 1908, Storage and Retrieval for Image and Video
databases. (1993).
[2] Carson, C., Thomas, M., Belongie, S., Hellerstein, J. M., & J. Malik.
“Blobworld: A System for Region-based Image Indexing and Retrieval”,
Proc. Third Int. Conf. on Visual Information Systems, June. (1999).
[3] Clément, A., Vigouroux, B. “Unsupervised segmentation of scenes
containing vegetation (Forsythia) and soil by hierarchical analysis of bidimensional
histograms”. Patt. Recogn. Lett, Vol. 24, 1951–1957.
(2003).
[4] Masmoudi, Lh., Zennouhi, R., & EL Ansari, M. Participation with a
chapter «Image Segmentation Based on Two Dimensional Histogram»
in a book «image segmentation» published by InTech, (2011).
[5] Cheng, H. D., Jiang, X. H., Sun, Y., Wang, J. “Color image
segmentation: advances and prospects”. Pattern Recognition, Vol.34,
No.6, 2259-2281. (2001). [6] Zennouhi, R.., Masmoudi, L. H. “A new 2D histogram scheme for color
image segmentation”. The Imaging Science Journal, Vol. 57, 260-265.
(2009).
[7] Sural, S., Qian, G., Pramanik, S. “Segmentation and histogram
generation using the HSV color space for image retrieval”, Proc. Int.
Conf. on Image processing: ICIP’02, Rochester, NY, USA, IEEE, Vol.
2, pp. II589–II592. (2002).
[8] Abutaleb, A. S. “Automatic thresholding of gray-level pictures using
two-dimensional entropy”. Journal of Computer Vision, Graphic and
Image Process, Vol. 47, 22–32. (1989).
[9] Zhang, Y. F., Zhang, Y. “Another Method of Building 2D Entropy to
Realize Automatic Segmentation”. Journal of Physics Conference
Series, Vol. 48, 303–307. (2006).
[10] Swain M.J., Ballard D.H., “Color indexing”. International Journal of
Computer Vision, vol. 7, no. 1, pp. 11-22. (1991).
[11] Stricker, M., Orengo, M. “Similarity of Color Images”, In Proceedings
of SPIE, Vol. 2420 (Storage and Retrieval of Image and Video
Databases III), SPIE Press, Feb. (1995).
[12] Iqbal, Q., Aggarwal, J. K. (2002), “Combining structure, color and
texture for image retrieval: a performance evaluation”. Proc. of
International Conference on Pattern Recognition (ICPR), Quebec
(Canada). (2002).
[13] Penn state university’s web page for modeling objects, concepts, and
aesthetics in images project. Online available at:
http://wang.ist.psu.edu/docs/related/.
[14] Coil (Columbia Object Image Library)dataset, Online available at:
http://www1.cs.columbia.edu/CAVE/research/softlib/
[15] The Fei-Fei dataset, Online available at:
http://www.vision.caltech.edu/feifeili/Datasets.htm.
[1] Niblack, W., Barber, R., Equitz, W., Flickner, M., Glasman, E.,
Petkovic, D., Yanker, P., Faloutsos, C., & Taubin, G. “The QBIC
Project: Querying Images by Content Using 37 Color, Texture, and
Shape”. SPIE Int. Symp. On Electronic Imaging: Science and
Technology Conf. 1908, Storage and Retrieval for Image and Video
databases. (1993).
[2] Carson, C., Thomas, M., Belongie, S., Hellerstein, J. M., & J. Malik.
“Blobworld: A System for Region-based Image Indexing and Retrieval”,
Proc. Third Int. Conf. on Visual Information Systems, June. (1999).
[3] Clément, A., Vigouroux, B. “Unsupervised segmentation of scenes
containing vegetation (Forsythia) and soil by hierarchical analysis of bidimensional
histograms”. Patt. Recogn. Lett, Vol. 24, 1951–1957.
(2003).
[4] Masmoudi, Lh., Zennouhi, R., & EL Ansari, M. Participation with a
chapter «Image Segmentation Based on Two Dimensional Histogram»
in a book «image segmentation» published by InTech, (2011).
[5] Cheng, H. D., Jiang, X. H., Sun, Y., Wang, J. “Color image
segmentation: advances and prospects”. Pattern Recognition, Vol.34,
No.6, 2259-2281. (2001). [6] Zennouhi, R.., Masmoudi, L. H. “A new 2D histogram scheme for color
image segmentation”. The Imaging Science Journal, Vol. 57, 260-265.
(2009).
[7] Sural, S., Qian, G., Pramanik, S. “Segmentation and histogram
generation using the HSV color space for image retrieval”, Proc. Int.
Conf. on Image processing: ICIP’02, Rochester, NY, USA, IEEE, Vol.
2, pp. II589–II592. (2002).
[8] Abutaleb, A. S. “Automatic thresholding of gray-level pictures using
two-dimensional entropy”. Journal of Computer Vision, Graphic and
Image Process, Vol. 47, 22–32. (1989).
[9] Zhang, Y. F., Zhang, Y. “Another Method of Building 2D Entropy to
Realize Automatic Segmentation”. Journal of Physics Conference
Series, Vol. 48, 303–307. (2006).
[10] Swain M.J., Ballard D.H., “Color indexing”. International Journal of
Computer Vision, vol. 7, no. 1, pp. 11-22. (1991).
[11] Stricker, M., Orengo, M. “Similarity of Color Images”, In Proceedings
of SPIE, Vol. 2420 (Storage and Retrieval of Image and Video
Databases III), SPIE Press, Feb. (1995).
[12] Iqbal, Q., Aggarwal, J. K. (2002), “Combining structure, color and
texture for image retrieval: a performance evaluation”. Proc. of
International Conference on Pattern Recognition (ICPR), Quebec
(Canada). (2002).
[13] Penn state university’s web page for modeling objects, concepts, and
aesthetics in images project. Online available at:
http://wang.ist.psu.edu/docs/related/.
[14] Coil (Columbia Object Image Library)dataset, Online available at:
http://www1.cs.columbia.edu/CAVE/research/softlib/
[15] The Fei-Fei dataset, Online available at:
http://www.vision.caltech.edu/feifeili/Datasets.htm.
@article{"International Journal of Information, Control and Computer Sciences:70230", author = "Khalid Elasnaoui and Brahim Aksasse and Mohammed Ouanan", title = "Content-Based Color Image Retrieval Based On 2-D Histogram and Statistical Moments", abstract = "In this paper, we are interested in the problem of
finding similar images in a large database. For this purpose we
propose a new algorithm based on a combination of the 2-D
histogram intersection in the HSV space and statistical moments. The
proposed histogram is based on a 3x3 window and not only on the
intensity of the pixel. This approach overcome the drawback of the
conventional 1-D histogram which is ignoring the spatial distribution
of pixels in the image, while the statistical moments are used to
escape the effects of the discretisation of the color space which is
intrinsic to the use of histograms. We compare the performance of
our new algorithm to various methods of the state of the art and we
show that it has several advantages. It is fast, consumes little memory
and requires no learning. To validate our results, we apply this
algorithm to search for similar images in different image databases.", keywords = "2-D histogram, Statistical moments, Indexing,
Similarity distance, Histograms intersection.", volume = "9", number = "2", pages = "603-5", }
