Standard Deviation of Mean and Variance of Rows and Columns of Images for CBIR
This paper describes a novel and effective approach to content-based image retrieval (CBIR) that represents each image in the database by a vector of feature values called “Standard deviation of mean vectors of color distribution of rows and columns of images for CBIR". In many areas of commerce, government, academia, and hospitals, large collections of digital images are being created. This paper describes the approach that uses contents as feature vector for retrieval of similar images. There are several classes of features that are used to specify queries: colour, texture, shape, spatial layout. Colour features are often easily obtained directly from the pixel intensities. In this paper feature extraction is done for the texture descriptor that is 'variance' and 'Variance of Variances'. First standard deviation of each row and column mean is calculated for R, G, and B planes. These six values are obtained for one image which acts as a feature vector. Secondly we calculate variance of the row and column of R, G and B planes of an image. Then six standard deviations of these variance sequences are calculated to form a feature vector of dimension six. We applied our approach to a database of 300 BMP images. We have determined the capability of automatic indexing by analyzing image content: color and texture as features and by applying a similarity measure Euclidean distance.
[1] C. Faloutsos, R. Barber,M. Flickner, J. Hafner,W. Niblack, D. Petkovic,
and W. Equitz, "Efficient and effective querying by image content," J.
Intell. Inf. Syst., vol. 3, no. 3-4, pp. 231-262, 1994.
[2] A. Gupta and R. Jain, "Visual information retrieval," Commun.
ACM,vol. 40, no. 5, pp. 70-79, 1997.
[3] J. R. Smith and S.-F. Chang, "VisualSEEK: a fully automated
contentbased query system," in Proc. 4th ACM Int. Conf. Multimedia,
1996, pp. 87-98.
[4] D. L. Swets and J. Weng, "Using discriminant eigenfeatures for image
retrieval," IEEE Trans. Pattern Anal. Mach. Intell., vol. 18, no. 8, pp.
831-837, Aug. 1996.
[5] C. Carson, S. Belongie, H. Greenspan, and J. Malik, "Blobworld: image
segmentation using expectation-maximization and its application to
image querying," IEEE Trans. Pattern Anal. Mach. Intell., vol. 24, no. 8,
pp. 1026-1038, Aug. 2002.
[6] Y. Chen and J. Z.Wang, "A Region-Based Fuzzy Feature Matching
Approach to Content-Based Image Retrieval," IEEE Trans. Pattern
Anal.Mach. Intell., vol. 24, no. 9, pp. 1252-1267, Sep. 2002.
[7] W. Y. Ma and B. Manjunath, "NeTta: a toolbox for navigating large
image databases," in Proc. IEEE Int. Conf. Image Processing, 1997,
pp.568-571.
[8] J. Z. Wang, J. Li, and G. Wiederhold, "SIMPLIcity: semantics-sensitive
integrated matching for picture libraries," IEEE Trans. Pattern
Anal.Mach. Intell., vol. 23, no. 9, pp. 947-963, Sep. 2001.
[9] R. W. Picard and T. P. Minka, "Vision texture for annotation," J.
Multimedia Syst., vol. 3, no. 1, pp. 3-14, 1995.
[10] S. Santini and R. Jain, "Similarity measures," IEEE Trans. Pattern
Anal.Mach. Intell., vol. 21, no. 9, pp. 871-883, Sep. 1999.
[11] C. Schmid and R. Mohr, "Local grayvalue invariants for image
retrieval," IEEE Trans. Pattern Anal. Mach. Intell., vol. 19, no. 5, pp.
530-535, May 1997.
[12] M. J. Swain and B. H. Ballard, "Color indexing," Int. J. Comput.
Vis.,vol. 7, no. 1, pp. 11-32, 1991.
[13] J. Hafner, H. S. Sawhney, W. Equitz, M. Flickner, and W. Niblack, "Efficient
color histogram indexing for quadratic form distance functions,"
IEEE Trans. Pattern Anal. Mach. Intell., vol. 17, no. 7, pp. 729-736, Jul.
1995.
[14] Y. Rubner, L. J. Guibas, and C. Tomasi, "The earth mover-s distance,
multi-dimensional scaling, and color-based image retrieval," in
Proc.DARPA Image Understanding Workshop, May 1997, pp. 661-668.
[15] J. G. Dy, C. E. Brodley,A. Kak, C. Shyu L. S. Broderick "The
Customized-Queries Approach to CBIR Using EM", IEEE Conference
on Computer Vision and Pattern Recognition, 1999.
[16] M. Flickner, "Query image and video by content: The QBIC
system,"IEEE Computer, vol. 28, no. 9, pp. 23-32, Sep. 1995.
[17] Remco C. Veltkamp, Mirela Tanase Department of Computing Science,
Utrecht University, "Content-Based Image Retrieval Systems:A Survey"
Revised and extended version of Technical Report UU-CS- 2000-34,
October October 28, 2002.
[18] Yixin Chen, Member, IEEE, James Z. Wang, Member, IEEE, And
Robert Krovetz CLUE: Cluster-Based Retrieval Of Images By
Unsupervised Learning IEEE Transactions on Image Processing,
Vol.14, No. 8, August 2005 1187
[19] Qasim Iqbal and J. K. Aggarwal, "Cires: A System For Content-Based
Retrieval In Digital Image Libraries" Seventh International Conference
on Control, Automation, Robotics And Vision (ICARCV-02), Dec 2002,
Singapore.
[20] Guoping Qiu "Color Image Indexing Using BTC"IEEE transactions on
image processing, vol. 12, no. 1, January 2003.
[21] Zur Erlangung des Doktorgrades "Feature Histograms for Content-
Based Image Retrieval" der Fakult¨at f¨ur Angewandte Wissenschaften
an der Albert-Ludwigs-Universit¨at Freiburg im Breisgau 2002
[22] Y. Rui and T. S. Huang, "Image retrieval: Current techniques, promising
directions, and open issues," J. Vis. Commun. Image Repres., vol.
10, pp.39-62, Oct. 1999.
[23] Young Deok Chun, Sang Yong Seo, and Nam Chul Kim "Image
retrieval Using BDIP and BVLC Moments" IEEE transactions on
circuits and systems for video technology, vol. 13, no. 9, september
2003.
[24] Rafael Gonzalez, Richard Woods, "Digital Image Processing" Second
Edition Pearson Education(Sinagpore Pte. Ltd.) 2003.
[1] C. Faloutsos, R. Barber,M. Flickner, J. Hafner,W. Niblack, D. Petkovic,
and W. Equitz, "Efficient and effective querying by image content," J.
Intell. Inf. Syst., vol. 3, no. 3-4, pp. 231-262, 1994.
[2] A. Gupta and R. Jain, "Visual information retrieval," Commun.
ACM,vol. 40, no. 5, pp. 70-79, 1997.
[3] J. R. Smith and S.-F. Chang, "VisualSEEK: a fully automated
contentbased query system," in Proc. 4th ACM Int. Conf. Multimedia,
1996, pp. 87-98.
[4] D. L. Swets and J. Weng, "Using discriminant eigenfeatures for image
retrieval," IEEE Trans. Pattern Anal. Mach. Intell., vol. 18, no. 8, pp.
831-837, Aug. 1996.
[5] C. Carson, S. Belongie, H. Greenspan, and J. Malik, "Blobworld: image
segmentation using expectation-maximization and its application to
image querying," IEEE Trans. Pattern Anal. Mach. Intell., vol. 24, no. 8,
pp. 1026-1038, Aug. 2002.
[6] Y. Chen and J. Z.Wang, "A Region-Based Fuzzy Feature Matching
Approach to Content-Based Image Retrieval," IEEE Trans. Pattern
Anal.Mach. Intell., vol. 24, no. 9, pp. 1252-1267, Sep. 2002.
[7] W. Y. Ma and B. Manjunath, "NeTta: a toolbox for navigating large
image databases," in Proc. IEEE Int. Conf. Image Processing, 1997,
pp.568-571.
[8] J. Z. Wang, J. Li, and G. Wiederhold, "SIMPLIcity: semantics-sensitive
integrated matching for picture libraries," IEEE Trans. Pattern
Anal.Mach. Intell., vol. 23, no. 9, pp. 947-963, Sep. 2001.
[9] R. W. Picard and T. P. Minka, "Vision texture for annotation," J.
Multimedia Syst., vol. 3, no. 1, pp. 3-14, 1995.
[10] S. Santini and R. Jain, "Similarity measures," IEEE Trans. Pattern
Anal.Mach. Intell., vol. 21, no. 9, pp. 871-883, Sep. 1999.
[11] C. Schmid and R. Mohr, "Local grayvalue invariants for image
retrieval," IEEE Trans. Pattern Anal. Mach. Intell., vol. 19, no. 5, pp.
530-535, May 1997.
[12] M. J. Swain and B. H. Ballard, "Color indexing," Int. J. Comput.
Vis.,vol. 7, no. 1, pp. 11-32, 1991.
[13] J. Hafner, H. S. Sawhney, W. Equitz, M. Flickner, and W. Niblack, "Efficient
color histogram indexing for quadratic form distance functions,"
IEEE Trans. Pattern Anal. Mach. Intell., vol. 17, no. 7, pp. 729-736, Jul.
1995.
[14] Y. Rubner, L. J. Guibas, and C. Tomasi, "The earth mover-s distance,
multi-dimensional scaling, and color-based image retrieval," in
Proc.DARPA Image Understanding Workshop, May 1997, pp. 661-668.
[15] J. G. Dy, C. E. Brodley,A. Kak, C. Shyu L. S. Broderick "The
Customized-Queries Approach to CBIR Using EM", IEEE Conference
on Computer Vision and Pattern Recognition, 1999.
[16] M. Flickner, "Query image and video by content: The QBIC
system,"IEEE Computer, vol. 28, no. 9, pp. 23-32, Sep. 1995.
[17] Remco C. Veltkamp, Mirela Tanase Department of Computing Science,
Utrecht University, "Content-Based Image Retrieval Systems:A Survey"
Revised and extended version of Technical Report UU-CS- 2000-34,
October October 28, 2002.
[18] Yixin Chen, Member, IEEE, James Z. Wang, Member, IEEE, And
Robert Krovetz CLUE: Cluster-Based Retrieval Of Images By
Unsupervised Learning IEEE Transactions on Image Processing,
Vol.14, No. 8, August 2005 1187
[19] Qasim Iqbal and J. K. Aggarwal, "Cires: A System For Content-Based
Retrieval In Digital Image Libraries" Seventh International Conference
on Control, Automation, Robotics And Vision (ICARCV-02), Dec 2002,
Singapore.
[20] Guoping Qiu "Color Image Indexing Using BTC"IEEE transactions on
image processing, vol. 12, no. 1, January 2003.
[21] Zur Erlangung des Doktorgrades "Feature Histograms for Content-
Based Image Retrieval" der Fakult¨at f¨ur Angewandte Wissenschaften
an der Albert-Ludwigs-Universit¨at Freiburg im Breisgau 2002
[22] Y. Rui and T. S. Huang, "Image retrieval: Current techniques, promising
directions, and open issues," J. Vis. Commun. Image Repres., vol.
10, pp.39-62, Oct. 1999.
[23] Young Deok Chun, Sang Yong Seo, and Nam Chul Kim "Image
retrieval Using BDIP and BVLC Moments" IEEE transactions on
circuits and systems for video technology, vol. 13, no. 9, september
2003.
[24] Rafael Gonzalez, Richard Woods, "Digital Image Processing" Second
Edition Pearson Education(Sinagpore Pte. Ltd.) 2003.
@article{"International Journal of Information, Control and Computer Sciences:55212", author = "H. B. Kekre and Kavita Patil", title = "Standard Deviation of Mean and Variance of Rows and Columns of Images for CBIR", abstract = "This paper describes a novel and effective approach to content-based image retrieval (CBIR) that represents each image in the database by a vector of feature values called “Standard deviation of mean vectors of color distribution of rows and columns of images for CBIR". In many areas of commerce, government, academia, and hospitals, large collections of digital images are being created. This paper describes the approach that uses contents as feature vector for retrieval of similar images. There are several classes of features that are used to specify queries: colour, texture, shape, spatial layout. Colour features are often easily obtained directly from the pixel intensities. In this paper feature extraction is done for the texture descriptor that is 'variance' and 'Variance of Variances'. First standard deviation of each row and column mean is calculated for R, G, and B planes. These six values are obtained for one image which acts as a feature vector. Secondly we calculate variance of the row and column of R, G and B planes of an image. Then six standard deviations of these variance sequences are calculated to form a feature vector of dimension six. We applied our approach to a database of 300 BMP images. We have determined the capability of automatic indexing by analyzing image content: color and texture as features and by applying a similarity measure Euclidean distance.
", keywords = "Standard deviation Image retrieval, color
distribution, Variance, Variance of Variance, Euclidean distance.", volume = "3", number = "3", pages = "679-4", }
