A New Approach to Region Based Image Retrieval using Shape Adaptive Discrete Wavelet Transform

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1. Department of Electronics, Djillali Liabes University, Sidi Bel-Abbes, Algeria

2. UEB INSA IETR D├ępartement Image et Automatique, 35708 Rennes, France

* Corresponding author.

DOI: https://doi.org/10.5815/ijigsp.2016.01.01

Received: 14 Sep. 2015 / Revised: 15 Oct. 2015 / Accepted: 27 Nov. 2015 / Published: 8 Jan. 2016

Index Terms

Content-based image retrieval (CBIR), DWT, Region-based image retrieval (RBIR), SA-DWT


In this paper, we present an efficient region-based image retrieval method, which uses multi-features color, texture and edge descriptors. In contrast to recent image retrieval methods, which use discrete wavelet transform (DWT), we propose using shape adaptive discrete wavelet transform (SA-DWT). The advantage of this method is that the number of coefficients after transformation is identical to the number of pixels in the original region. Since image data is often stored in compressed formats: JPEG 2000, MPEG 4…; constructing image histograms directly in the compressed domain, allows accelerating the retrieval operation time, and reducing computing complexities. Moreover, SA-DWT represents the best way to exploit the coefficients characteristics, and properties such as the correlation. Characterizing image regions without any conversion or modification is first addressed. Using edge descriptor to complement image region characterizing is then introduced. Experimental results show that the proposed method outperforms content based image retrieval methods and recent region based image retrieval methods.

Cite This Paper

Lakhdar BELHALLOUCHE, Kamel BELLOULATA, Kidiyo KPALMA,"A New Approach to Region Based Image Retrieval using Shape Adaptive Discrete Wavelet Transform", International Journal of Image, Graphics and Signal Processing(IJIGSP), Vol.8, No.1, pp.1-14, 2016. DOI: 10.5815/ijigsp.2016.01.01


[1]K. Belloulata, L. Belhallouche, A. Belalia, K. Kpalma, "Region based image retrieval using shape-adaptive DCT", China SIP, pp. 470-474, July 2014.

[2]L. Belallouche, K. Belloulata, K. Kpalma. "Light field retrieval in compressed domain", 21st international conference in central Europe on computer graphics, visualization and computer vision, pp. 45-48. 2013.

[3]H. Abrishami, M. T. T. Khajoie, A. H. Rouhi, M. S. Tarzjan, "Wavelet correlogram: A new approach for image indexing and retrieval Pattern Recognition", Pattern Recognition journal, vol 38, pp. 2506 – 2518. 2005.

[4]W. Y. Gao, K. Chan. "A Review of Region-Based Image Retrieval". Journal Signal Processing System, pp. 59:143–161. 2010.

[5]Tsai, C.-F., McGarry, K., & Tait, J. "Image classification using hybrid neural network". In Proceedings of the ACM SIGIR conference on research and development in information retrieval, pp. 431–432, 2003.

[6]E R Vimina1 and K Poulose Jacob, "A Sub-block Based Image Retrieval Using Modified Integrated Region Matching", International Journal of Computer Science Issues, Vol.10, Issue 1,No 2, pp. 686-692, 2013.

[7]D. G. Lowe, "Object recognition from local scale-invariant features", international conference on computer vision, pp.1150–1157, 1999.

[8]J. Yu, Z. Qin, T. Wan, X. Zhang, "Feature integration analysis of bag-of-features model for image retrieval", Neuro computing journal, pp. 355-364, 2013.

[9]D. Zhang, Md. Monirul Islam, G. Lu, I. J. Sumana, "rotation invariant curvelet features for region based image retrieval", International Journal of Computer Vision , pp. 187-201, 2012.

[10]K. Prasanthi Jasmine, P. Rajesh Kumar, "color and rotated M-Band dual tree complex wavelet transform features for image retrieval", international journal of image, graphics and signal processing, pp. 1-10, 2014.

[11]C-Y. Wang, X. Zhang, R. Shan, X. Zhou,"grading image retrieval based on DCT and DWT compressed domains using Low-Level features", Journal of Communications, pp. 64-73, 2015.

[12]S. I. Ibrahim, A. Abuhaiba, A. Ruba, A.Salamah, "efficient global and region content based image retrieval", international journal of image, graphics and signal processing, pp. 38-46, 2012.

[13]M. Li, R. C. Staunton, "optimum Gabor filter design and local binary patterns for texture segmentation", journal pattern recognition, pp. 664-672, 2008. 

[14]K. Prasanthi Jasmine, P. Rajesh Kumar, "color and local maximum edge patterns histogram for content based image retrieval", international journal of intelligent systems and applications, pp. 66-74, 2014.

[15]Y. Chen, J. Wang, "A region-based fuzzy feature matching approach to content-based image retrieval", IEEE transactions on pattern analysis and machine intelligence, pp. 1252–1267, 2002.

[16]C. Huang, Y. Han, Y. Zhang, "A method for object-based color image retrieval", 9th international conference on fuzzy systems and knowledge discovery, pp. 1659 – 1663, 2012.

[17]Y. Sun, S. Ozawa, "HIRBIR: A hierarchical approach to region-based image retrieval", multimedia systems journal, pp. 559–569, 2005.

[18]A. Natsev, R. Rastogi, K. Shim, "WALRUS: A similarity retrieval algorithm for image databases", IEEE transactions on knowledge and data engineering, pp. 301 – 316, 2004.

[19]C. Bai, J Zhang, Z Liu, W-L Zhao, "K-means based histogram using multiresolution feature vectors for color texture database retrieval", Journal multimedia tools and applications, pp. 1469-1488, 2015.

[20]S. Li, W. Li, "shape adaptive discrete wavelet transforms for arbitrarily shaped visual object coding", IEEE transactions on circuits and systems for video technology, pp. 725 – 743, 2000.

[21]C-T. Huang, P-C. Tseng, L-G. Chen, "VLSI architecture for lifting-based shape-adaptive discrete wavelet Transform with Odd-symmetric filters", Journal of VLSI signal processing, pp. 175–188, 2005.

[22]S. Murala, R. P. Maheshwari, R. Balasubramanian, "Directional local extrema patterns: a new descriptor for content based image retrieval", International journal of multimedia information retrieval, pp. 191–203, 2012.

[23]D Gabor, "Theory of communication. Part 1: The analysis of information", Journal of the institution of electrical engineers, -Part III. - IET, pp. 429-441, 1946.

[24]K. Seetharaman, M. Kamarasan, "Statistical framework for content-based medical image retrieval based on wavelet orthogonal polynomial model with multiresolution structure", international journal multimedia information retrieval, pp. 53–66, 2014.

[25]S. Mallat, "A theory for multiresolution signal decomposition: The wavelet representation", IEEE transactions on pattern analysis and machine intelligence, pp. 674–693, 1989.



[28]https://sites.google.com/site/dctresearch/Home/content based-image-retrieval.2014.

[29]J.Z. Wang, J. Li, G. Wiederhold, "Simplicity Semantics-sensitive integrated matching for Picture librairies", IEEE Transactions on pattern analysis and machine intelligence, pp. 1- 17, 2001.

[30]N. Jhanwara, S. Chaudhuri, G. Seetharamanc, and B. Zavidovique, "Content based image retrieval using motif co-occurrence matrix", Image and Vision Computing, pp. 1211–1220. 2004.

[31]Subrahmanyam Murala, R. P. Maheshwari, R. Balasubramanian, "Expert content-based image retrieval system using robust local patterns", J. Visual communication and image representation, pp. 1324–1334, 2014. 

[32]J. Li, J. Wang, and G. Wiederhold, "Integrated region matching for image retrieval," in Proc. 2000 ACM Multimedia Conf., Los Angeles, pp. 147-156, 2000. 

[33]R. Zhang and Z. Zhang, "A clustering based approach to efficient image retrieval," in Proc. 14th IEEE Int. Conf. Tools with Artificial Intell. (ICTAI'02), Washington, DC, pp. 339-346, 2002.

[34]A. Rao, R. Srihari, and Z. Zhang, "Geometric histogram: A distribution of geometric configuration of color subsets," in Proc. SPIE Conf. Electronic Imaging 2000, vol. 3964-09, San Jose, CA, pp. 91-101, 2000.

[35]D. Lakshmi, A. Damodaram, M. Sreenivasa, and J. Lal, "Content based image retrieval using signature based similarity search," Indian J. Science and Technology, vol. 1, no. 5, pp. 80-92, Oct. 2008.

[36]Y. Liu, X. Zhou, W-Y. Ma, "Extracting texture features from arbitrary-shaped regions for image retrieval", Multimedia and Expo, ICME, IEEE International Conference, pp. 1891 – 1894, 2004. 

[37]Y. Liu, D. Zhang, G. Lu, W-Y. Ma, "Study on texture feature extraction in region-based image retrieval system", Multi-Media Modelling Conference, pp. 264-271, 2006.