A Universal Model for Content-Based Image Retrieval
In this paper a novel approach for generalized image
retrieval based on semantic contents is presented. A combination of
three feature extraction methods namely color, texture, and edge
histogram descriptor. There is a provision to add new features in
future for better retrieval efficiency. Any combination of these
methods, which is more appropriate for the application, can be used
for retrieval. This is provided through User Interface (UI) in the
form of relevance feedback. The image properties analyzed in this
work are by using computer vision and image processing algorithms.
For color the histogram of images are computed, for texture cooccurrence
matrix based entropy, energy, etc, are calculated and for
edge density it is Edge Histogram Descriptor (EHD) that is found.
For retrieval of images, a novel idea is developed based on greedy
strategy to reduce the computational complexity. The entire system
was developed using AForge.Imaging (an open source product),
MATLAB .NET Builder, C#, and Oracle 10g. The system was tested
with Coral Image database containing 1000 natural images and
achieved better results.
[1] Tristan Glatard, John Montagnat, "Texture based Medical image
indexing and retrieval: application to cardiac images".
[2] B. S. Manjunath, Jens-Rainer Ohm, Vinod V. Vasudevan, and Akio
Yamada, "Color and Texture Descriptors". In: IEEE Transactions on
Circuits and Systems for Video Technology, Vol. 11, No. 6, June 2001,
pp. 70-715.
[3] Zhe-Ming Lu1, Su-Zhi Li, and Hans Burkhardt, "A Content-Based
Image Retrieval Scheme in JPEG Compressed Domain", International
Journal of Innovative Computing, Information and Control ICIC
International 2006 ISSN 1349-4198, Volume 2, Number 4, August
2006, pp. 831-839.
[4] Minyoung Eom, and Yoonsik Choe, "Fast Extraction of Edge Histogram
in DCT Domain based on MPEG7", Proceedings of World Academy of
Science, Engineering and Technology Volume 9 November 2005 ISSN
1307-6884, pp. 209-212.
[5] Son Lam Phung and Abdesselam Bouzerdoum, "A New Image Feature
for Fast Detection of People in Images", International Journal of 2007
Institute for Scientific Information and Systems Sciences Computing and
Information Volume 3, Number 3, pp. 383-391.
[6] Paul Stefan, et al.: Segmentation of Natural Images for CBIR.C. J.
Kaufman, Rocky Mountain Research Lab., Boulder, CO, private
communication, May 1995.
[7] P. S. Hiremath , Jagadeesh Pujari, "Content Based Image Retrieval using
Color, Texture and Shape features", 15th International Conference on
Advanced Computing and Communications, IEEE Computer Society
2007, pp. 780-784.
[8] Remco C. Veltcamp, Mirela Tanse, "Content Based Image Retrieval
Systems". A Survey, Technical Report UU-CS-2000-34, October 2000,
pp. 1-62.
[9] Mustafa Ozden and Ediz Polat, "Image Segmentation using Color and
Texture features".
[10] John Montagnat, et al, "Texture-based Medical Image Indexing and
Retrieval on Grids", Medical Imaging technology, vol 25 No. 5 Nov
2007. pp. 333-338.
[11] C. R. Shyu, et. al, "Local versus Global Features for Content-Based
Image Retrieval", IEEE Workshop on Content-Based Access of Image
and Video Libraries, 1998.
[12] Roger Weber and Michael Mlivoncic, "Efficient Region-Based Image
Retrieval", ACM CIKM '03 November 3-8, 2003, USA.
[13] S. L. Phung and A. Bouzerdoum, "Detecting People in Images: An Edge
Density Approach", IEEE, ICASSP 2007. pp. 1229-1232.
[14] Bohyung Han, Changjiang Yang, et al, "Bayesian Filtering and Integral
Image for Visual Tracking".
[15] Vincent Arvis, Christophe Debain, et. al, "Generalization of the
Cooccurrence Matrix for Color Images Application to Color Texture
Classification", Image AnalStereol 2004, pp. 63-72.
[16] Alberto Amato, Vincenzo Di Lecce, "Edge Detection Techniques in
Image Retrieval: The Semantic Meaning of Edge", 4th EURASIP
Conference on Video/Image Processing and Multimedia
Communications, Zagreb, Croatia. pp. 143-148.
[17] Thomas M. Lehmann, et al, "Automatic categorization of medical
images for content-based retrieval and data mining", Computerized
Medical Imaging and Graphics. Elsevier 2004. pp. 143-155.
[18] Dong Yin, Jia Pan, et al, "Medical Image Categorization based on
Gaussian Mixture Model", IEEE 2008 International Conference on
BioMedical Engineering and Informatics, pp. 128-131.
[19] Dr. Fuhui Long, Dr. Hongjiang Zhang and Prof. David Dagan Feng,
"Fundamentals of Content-Based Image Retrieval" -
http://research.microsoft.com/asia/dload_files/group/mcomputing/2003
P/ch01_Long_v40-proof.pdf
[1] Tristan Glatard, John Montagnat, "Texture based Medical image
indexing and retrieval: application to cardiac images".
[2] B. S. Manjunath, Jens-Rainer Ohm, Vinod V. Vasudevan, and Akio
Yamada, "Color and Texture Descriptors". In: IEEE Transactions on
Circuits and Systems for Video Technology, Vol. 11, No. 6, June 2001,
pp. 70-715.
[3] Zhe-Ming Lu1, Su-Zhi Li, and Hans Burkhardt, "A Content-Based
Image Retrieval Scheme in JPEG Compressed Domain", International
Journal of Innovative Computing, Information and Control ICIC
International 2006 ISSN 1349-4198, Volume 2, Number 4, August
2006, pp. 831-839.
[4] Minyoung Eom, and Yoonsik Choe, "Fast Extraction of Edge Histogram
in DCT Domain based on MPEG7", Proceedings of World Academy of
Science, Engineering and Technology Volume 9 November 2005 ISSN
1307-6884, pp. 209-212.
[5] Son Lam Phung and Abdesselam Bouzerdoum, "A New Image Feature
for Fast Detection of People in Images", International Journal of 2007
Institute for Scientific Information and Systems Sciences Computing and
Information Volume 3, Number 3, pp. 383-391.
[6] Paul Stefan, et al.: Segmentation of Natural Images for CBIR.C. J.
Kaufman, Rocky Mountain Research Lab., Boulder, CO, private
communication, May 1995.
[7] P. S. Hiremath , Jagadeesh Pujari, "Content Based Image Retrieval using
Color, Texture and Shape features", 15th International Conference on
Advanced Computing and Communications, IEEE Computer Society
2007, pp. 780-784.
[8] Remco C. Veltcamp, Mirela Tanse, "Content Based Image Retrieval
Systems". A Survey, Technical Report UU-CS-2000-34, October 2000,
pp. 1-62.
[9] Mustafa Ozden and Ediz Polat, "Image Segmentation using Color and
Texture features".
[10] John Montagnat, et al, "Texture-based Medical Image Indexing and
Retrieval on Grids", Medical Imaging technology, vol 25 No. 5 Nov
2007. pp. 333-338.
[11] C. R. Shyu, et. al, "Local versus Global Features for Content-Based
Image Retrieval", IEEE Workshop on Content-Based Access of Image
and Video Libraries, 1998.
[12] Roger Weber and Michael Mlivoncic, "Efficient Region-Based Image
Retrieval", ACM CIKM '03 November 3-8, 2003, USA.
[13] S. L. Phung and A. Bouzerdoum, "Detecting People in Images: An Edge
Density Approach", IEEE, ICASSP 2007. pp. 1229-1232.
[14] Bohyung Han, Changjiang Yang, et al, "Bayesian Filtering and Integral
Image for Visual Tracking".
[15] Vincent Arvis, Christophe Debain, et. al, "Generalization of the
Cooccurrence Matrix for Color Images Application to Color Texture
Classification", Image AnalStereol 2004, pp. 63-72.
[16] Alberto Amato, Vincenzo Di Lecce, "Edge Detection Techniques in
Image Retrieval: The Semantic Meaning of Edge", 4th EURASIP
Conference on Video/Image Processing and Multimedia
Communications, Zagreb, Croatia. pp. 143-148.
[17] Thomas M. Lehmann, et al, "Automatic categorization of medical
images for content-based retrieval and data mining", Computerized
Medical Imaging and Graphics. Elsevier 2004. pp. 143-155.
[18] Dong Yin, Jia Pan, et al, "Medical Image Categorization based on
Gaussian Mixture Model", IEEE 2008 International Conference on
BioMedical Engineering and Informatics, pp. 128-131.
[19] Dr. Fuhui Long, Dr. Hongjiang Zhang and Prof. David Dagan Feng,
"Fundamentals of Content-Based Image Retrieval" -
http://research.microsoft.com/asia/dload_files/group/mcomputing/2003
P/ch01_Long_v40-proof.pdf
@article{"International Journal of Information, Control and Computer Sciences:58503", author = "S. Nandagopalan and Dr. B. S. Adiga and N. Deepak", title = "A Universal Model for Content-Based Image Retrieval", abstract = "In this paper a novel approach for generalized image
retrieval based on semantic contents is presented. A combination of
three feature extraction methods namely color, texture, and edge
histogram descriptor. There is a provision to add new features in
future for better retrieval efficiency. Any combination of these
methods, which is more appropriate for the application, can be used
for retrieval. This is provided through User Interface (UI) in the
form of relevance feedback. The image properties analyzed in this
work are by using computer vision and image processing algorithms.
For color the histogram of images are computed, for texture cooccurrence
matrix based entropy, energy, etc, are calculated and for
edge density it is Edge Histogram Descriptor (EHD) that is found.
For retrieval of images, a novel idea is developed based on greedy
strategy to reduce the computational complexity. The entire system
was developed using AForge.Imaging (an open source product),
MATLAB .NET Builder, C#, and Oracle 10g. The system was tested
with Coral Image database containing 1000 natural images and
achieved better results.", keywords = "Content Based Image Retrieval (CBIR), Cooccurrencematrix, Feature vector, Edge Histogram Descriptor(EHD), Greedy strategy.", volume = "2", number = "10", pages = "3431-4", }
