Gaussian Density and HOG with Content Based Image Retrieval System – A New Approach
Content-based image retrieval (CBIR) uses the
contents of images to characterize and contact the images. This paper
focus on retrieving the image by separating images into its three color
mechanism R, G and B and for that Discrete Wavelet Transformation
is applied. Then Wavelet based Generalized Gaussian Density (GGD)
is practical which is used for modeling the coefficients from the
wavelet transforms. After that it is agreed to Histogram of Oriented
Gradient (HOG) for extracting its characteristic vectors with Relevant
Feedback technique is used. The performance of this approach is
calculated by exactness and it confirms that this method is wellorganized
for image retrieval.
[1] M. K. Mandal, T. Aboulnasr, and S. Panchanathan,, “Image Indexing
Using Moments and Wavelets”, IEEE Transactions on Consumer
Electronics, Vol. 42, No. 3, August 1996V. Gudivada and V. Raghavan.
Content-based image retrieval systems. IEEE Computer, 28(9):18 – 22,
September 1995.
[2] J. Hafner, H. S. Sawhney, W. Equitz, M. Flickner, and W. Niblack.
Efficient color histogram indexing for quadratic form distance functions.
IEEE Transactions on Pattern Analysis and Machine Intelligenc (PAMI),
17(7):729–736, 1995.
[3] G. Pass, R. Zabih, and J. Miller. Comparing images using color
coherence vectors. In ACM International Conference on Multimedia,
pages 65–73, Boston, MA, Nov. 1996. [4] B. S. Manjunath and W. Y. Ma, “Texture features for browsing and
retrieval of image data,” IEEE Trans. Pattern Recognit. Machine Intell.,
vol. 18, pp. 837–842, Aug. 1996.
[5] G. V. Wouwer, P. Scheunders, and D. V. Dyck, “Statistical texture
characterization from discrete wavelet representations,” IEEE Trans.
Image Processing, vol. 8, pp. 592–598, Apr. 1999.
[6] T. Randen and J. H. Husoy, “Filtering for texture classification: A
comparative study,” IEEE Trans. Pattern Recognit. Machine Intell., vol.
21, pp. 291–310, 1999..
[7] JPEG Committee. JPEG home page. (Online) http://www.jpeg.org.
[8] C. Jacobs, A. Finkelstein, and D. Salesin, “Fast multiresolution image
querying,” in Proc. SIGGRAPH Computer Graphics, Los Angeles, CA,
1995, pp. 278–280.
[9] M. Do, S. Ayer, and M. Vetterli, “Invariant image retrieval using
wavelet maxima moment,” in Proc. 3rd Int. Conf. Visual Information
Information Systems, 1999, pp. 451–458.
[10] S. Mallat, “A theory for multiresolution signal decomposition: The
wavelet representation,” IEEE Trans. Pattern Recognit. Mach. Intell.,
vol. 11, no. 7, pp. 674–693, Jul. 1989.
[11] P.S.Suhasini, Dr. K.sri rama krishna, Dr. I. V. Murali krishna,”cbir using
color histogram processing” Journal of Theoretical and Applied
Information Technology 2009.
[12] Vibha Bhandari and Sandeep B.Patil,CBIR Using DCT for Feature
Vector Generation,. International Journal of Application or Innovation in
Engineering & Management, Volume 1, Issue 2, October 2012.
[13] A. Smeulders, “Content-Based Image Retrieval at the End of the Early
Years,” IEEE Trans. Pattern Analysis and Machine Intelligence, vol. 22,
no. 12, pp. 1349- 1380, May. 2000.
[14] J. Caicedo, F. Gonzalez, E. Romero, E. triana, “Design of a Medical
Image Database with Content-Based Retrieval Capabilities,” In
Proceedings of the 2nd Pacific Rim conference on Advances in image
and video technology, Santiago, Chile, December 17-19, 2007.
[15] G. V. Wouwer, P. Scheunders, and D. V. Dyck, “Statistical texture
characterization from discrete wavelet representations,” IEEE Trans.
Image Processing, vol. 8, pp. 592–598, Apr. 1999.
[16] N. Vasconcelos and A. Lippman, “A unifying view of image similarity,”
in Proc. IEEE Int. Conf. Pattern Recognition (ICPR), Barcelona, Spain,
2000.
[17] G. V. Wouwer, P. Scheunders, and D. V. Dyck, “Statistical texture
characterization from discrete wavelet representations,” IEEE Trans.
Image Process., vol. 8, no. 4, pp. 592–598, Apr. 1999.
[18] M. N. Do and M. Vetterli, “Wavelet-based texture retrieval using
generalized Gaussian density and Kullback-Leibler distance,” IEEE
Trans. Image Process., vol. 11, no. 2, pp. 146–158, Feb. 2002.
[19] Pushpa B. Patil, Manesh B. Kokare(2011), “Relevance Feedback In
Content Based Image Retrieval: A Review”, Journal Of Applied
Computer Science & Mathematics, Vol.5, No. 10 .
[20] Dalal, N., Triggs, B.: Histograms of Oriented Gradients for Human
Detection. In: IEEE Conference on Computer Vision and Pattern
Recognition (CVPR) (2005)
[21] Ke, Y., Sukthankar, R.: PCA-SIFT: A more distinctive representation
for local image descriptors. In: Proc. of Computer Vision and Pattern
Recognition, Washington, pp. 66–75 (2004).
[1] M. K. Mandal, T. Aboulnasr, and S. Panchanathan,, “Image Indexing
Using Moments and Wavelets”, IEEE Transactions on Consumer
Electronics, Vol. 42, No. 3, August 1996V. Gudivada and V. Raghavan.
Content-based image retrieval systems. IEEE Computer, 28(9):18 – 22,
September 1995.
[2] J. Hafner, H. S. Sawhney, W. Equitz, M. Flickner, and W. Niblack.
Efficient color histogram indexing for quadratic form distance functions.
IEEE Transactions on Pattern Analysis and Machine Intelligenc (PAMI),
17(7):729–736, 1995.
[3] G. Pass, R. Zabih, and J. Miller. Comparing images using color
coherence vectors. In ACM International Conference on Multimedia,
pages 65–73, Boston, MA, Nov. 1996. [4] B. S. Manjunath and W. Y. Ma, “Texture features for browsing and
retrieval of image data,” IEEE Trans. Pattern Recognit. Machine Intell.,
vol. 18, pp. 837–842, Aug. 1996.
[5] G. V. Wouwer, P. Scheunders, and D. V. Dyck, “Statistical texture
characterization from discrete wavelet representations,” IEEE Trans.
Image Processing, vol. 8, pp. 592–598, Apr. 1999.
[6] T. Randen and J. H. Husoy, “Filtering for texture classification: A
comparative study,” IEEE Trans. Pattern Recognit. Machine Intell., vol.
21, pp. 291–310, 1999..
[7] JPEG Committee. JPEG home page. (Online) http://www.jpeg.org.
[8] C. Jacobs, A. Finkelstein, and D. Salesin, “Fast multiresolution image
querying,” in Proc. SIGGRAPH Computer Graphics, Los Angeles, CA,
1995, pp. 278–280.
[9] M. Do, S. Ayer, and M. Vetterli, “Invariant image retrieval using
wavelet maxima moment,” in Proc. 3rd Int. Conf. Visual Information
Information Systems, 1999, pp. 451–458.
[10] S. Mallat, “A theory for multiresolution signal decomposition: The
wavelet representation,” IEEE Trans. Pattern Recognit. Mach. Intell.,
vol. 11, no. 7, pp. 674–693, Jul. 1989.
[11] P.S.Suhasini, Dr. K.sri rama krishna, Dr. I. V. Murali krishna,”cbir using
color histogram processing” Journal of Theoretical and Applied
Information Technology 2009.
[12] Vibha Bhandari and Sandeep B.Patil,CBIR Using DCT for Feature
Vector Generation,. International Journal of Application or Innovation in
Engineering & Management, Volume 1, Issue 2, October 2012.
[13] A. Smeulders, “Content-Based Image Retrieval at the End of the Early
Years,” IEEE Trans. Pattern Analysis and Machine Intelligence, vol. 22,
no. 12, pp. 1349- 1380, May. 2000.
[14] J. Caicedo, F. Gonzalez, E. Romero, E. triana, “Design of a Medical
Image Database with Content-Based Retrieval Capabilities,” In
Proceedings of the 2nd Pacific Rim conference on Advances in image
and video technology, Santiago, Chile, December 17-19, 2007.
[15] G. V. Wouwer, P. Scheunders, and D. V. Dyck, “Statistical texture
characterization from discrete wavelet representations,” IEEE Trans.
Image Processing, vol. 8, pp. 592–598, Apr. 1999.
[16] N. Vasconcelos and A. Lippman, “A unifying view of image similarity,”
in Proc. IEEE Int. Conf. Pattern Recognition (ICPR), Barcelona, Spain,
2000.
[17] G. V. Wouwer, P. Scheunders, and D. V. Dyck, “Statistical texture
characterization from discrete wavelet representations,” IEEE Trans.
Image Process., vol. 8, no. 4, pp. 592–598, Apr. 1999.
[18] M. N. Do and M. Vetterli, “Wavelet-based texture retrieval using
generalized Gaussian density and Kullback-Leibler distance,” IEEE
Trans. Image Process., vol. 11, no. 2, pp. 146–158, Feb. 2002.
[19] Pushpa B. Patil, Manesh B. Kokare(2011), “Relevance Feedback In
Content Based Image Retrieval: A Review”, Journal Of Applied
Computer Science & Mathematics, Vol.5, No. 10 .
[20] Dalal, N., Triggs, B.: Histograms of Oriented Gradients for Human
Detection. In: IEEE Conference on Computer Vision and Pattern
Recognition (CVPR) (2005)
[21] Ke, Y., Sukthankar, R.: PCA-SIFT: A more distinctive representation
for local image descriptors. In: Proc. of Computer Vision and Pattern
Recognition, Washington, pp. 66–75 (2004).
@article{"International Journal of Information, Control and Computer Sciences:68945", author = "N. Shanmugapriya and R. Nallusamy", title = "Gaussian Density and HOG with Content Based Image Retrieval System – A New Approach", abstract = "Content-based image retrieval (CBIR) uses the
contents of images to characterize and contact the images. This paper
focus on retrieving the image by separating images into its three color
mechanism R, G and B and for that Discrete Wavelet Transformation
is applied. Then Wavelet based Generalized Gaussian Density (GGD)
is practical which is used for modeling the coefficients from the
wavelet transforms. After that it is agreed to Histogram of Oriented
Gradient (HOG) for extracting its characteristic vectors with Relevant
Feedback technique is used. The performance of this approach is
calculated by exactness and it confirms that this method is wellorganized
for image retrieval.
", keywords = "Content-Based Image Retrieval (CBIR), Relevant
Feedback, Histogram of Oriented Gradient (HOG), Generalized
Gaussian Density (GGD).", volume = "8", number = "11", pages = "2065-5", }
