The week's pick
Reseach Article
Feature Vectors based CBIR in Spatial and Transform Domain
| International Journal of Computer Applications |
| Foundation of Computer Science (FCS), NY, USA |
| Volume 60 - Number 19 |
| Year of Publication: 2012 |
| Authors: Swapna Borde, Udhav Bhosle |
10.5120/9810-4399
|
Swapna Borde, Udhav Bhosle . Feature Vectors based CBIR in Spatial and Transform Domain. International Journal of Computer Applications. 60, 19 ( December 2012), 34-42. DOI=10.5120/9810-4399
Abstract
This paper presents Content Based Image Retrieval Techniques based on feature vectors in Spatial Domain and Transform Domain. The feature extraction in spatial domain includes the CBIR techniques based on Gaussian Pyramid, Laplacian Pyramid and Steerable Pyramid. The feature extraction in transform domain includes the CBIR techniques based on Discrete Cosine Transform, Discrete Fourier Transform, Hadamard Transform and Wavelet Transform. Instead of using all the coefficients of images as feature vector for Content Based Image Retrieval, only two feature vectors such as mean and standard deviation are used. The feature vector size in transform domain is less as compared to feature vector size in spatial domain. All the CBIR techniques are implemented on a database having 648 images spread across 9 classes. For each CBIR technique, 27 queries (3 per class) are applied on the Image database and precision & Recall values are computed. The results have shown performance improvement with Discrete Fourier Transform, Wavelet Transform and Gaussian Pyramid as compared to other techniques at reduced computations.
References
- Guoping Qiu,” Color Image Indexing Using BTC,”IEEE Transactions on Image Processing, VOL.12, NO.1, pp.93-101, January 2003.
- B.G.Prasad, K.K. Biswas, and S. K.Gupta,” Region –based image retrieval using integrated color, shape, and location index,” computer vision an d image understanding, October 2003.
- Minh N. Do, Member, IEEE, and Martin Vetterli, Fellow, IEEE,” Wavelet-Based Texture Retrieval Using Generalized Gaussian Density and Kullback-Leibler Distance,” IEEE Transactions On Image Processing, VOL.11, NO.2, February 2002.
- Dr. Fuhui Long, Dr. Hongjiang Zhang and Prof. David Dagan Feng,” Fundamentals of Content-Based Image Retrieval,”
- Michael Eziashi Osadebey ,” Integrated content -based image retrieval using texture , shape and spatial information “,Master Thesis Report in Media Signal Processing , Department of Applied Physics and Electronics, Umea University, Umea Sweden .
- Rajashekhara,” Novel Image Retrieval Techniques: domain specific approaches,” Ph.D. Thesis Department of Electrical Engineering Indian Institute of Technology – Bombay, 2006.
- Guojun Lu and Shyhwei Teng,” A Novel Image Retrieval Technique based on Vector Quantization,” Technical Report, Gippsland School of computing and Information Technology, Monash University, Gippsland Campus, Churchill, Vic 3842.
- Sameer A. Nene, Shree K. Nayar and Hiroshi Murase,”Columbia Object Image Library(COIL-100)”, Technical Report
- Stian Edvardsen,”Classification of Images using color, CBIR Distance Measures and Genetic Programming, “Ph.D. Thesis , Master of science in Informatics, Norwegian university of science and Technology, Department of computer and Information science, June 2006.
- Rami Al-Tayeche & Ahmed Khalil,”CBIR: Content Based Image Retrieval,” Project Report, Department of systems and computer Engineering, Faculty of Engineering, Carleton University, April 4, 2003.
- Lei Zhu, Chun Tang, Aibing Rao and Aidong Zhang,”Using Thesaurus To Model Keyblock-Based Image Retrieval ,” Technical Report, Department of Computer Science and Engineering , State University of New York At Buffalo,Buffalo,NY 14260,USA.
- Swapna Borde , Dr. Udhav Bhosle ,” Image Retrieval Using Contourlet Transform,” International Journal of Computer Applications
- Swapna Borde , Dr. Udhav Bhosle ,” Image Retrieval Using Steerable Pyramid,” International Journal of Computer Applications
Index Terms
Keywords