Abstract
Complicated shapes can be effectively characterized using multi-resolution descriptors. One popular method is the Ridgelet transform which has enjoyed very little exposure in describing shapes for Content-based Image Retrieval (CBIR). Many of the existing Ridgelet transforms are only applied on images of size M×M. For M×N sized images, they need to be segmented into M×M sub-images prior to processing. A different number of orientations and cut-off points for the Radon transform parameters also need to be utilized according to the image size. This paper presents a new shape descriptor for CBIR based on Ridgelet transform which is able to handle images of various sizes. The utilization of the ellipse template for better image coverage and the normalization of the Ridgelet transform are introduced. For better retrieval, a template-option scheme is also introduced. Retrieval effectiveness obtained by the proposed method has shown to be higher compared to several previous descriptors.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
Similar content being viewed by others
References
Hare, J.S., Sinclair, P.A.S., Lewis, P.H., Martinez, K., Enser, P.G.B., Sandom, C.J.: Bridging the Semantic Gap in Multimedia Information Retrieval: Top-down and Bottom-up Approaches. In: 3rd Annual European Semantic Web Conference (2006)
Belongie, S., Malik, J., Puzicha, J.: Shape Matching and Object Recognition using Shape Contexts. IEEE Transactions on Pattern Analysis and Machine Intelligence 24(4), 509–522 (2002)
Wang, X., Feng, B., Bai, X., Liu, W., Latecki, L.J.: Bag of Contour Fragments for Robust Shape Classification. Pattern Recognition 47(6), 2116–2125 (2014)
Arslan, S., Ozyurek, E., Gunduz-Demir, C.: A Color and Shape-based Algorithm for Segmentation of White Blood Cells in Peripheral Blood and Bone Marrow Images. Cytometry Part A (2014)
Costa, L.D.F.D., Cesar Jr., R.M.: Shape Analysis and Classification: Theory and Practice. CRC Press LLC, Boca Raton (2001)
Koenderink, J.J.: The Structure of Images. Biological Cybernetics 50(5), 363–370 (1984)
Chen, G.Y., Bui, T.D., Krzyżak, A.: Rotation Invariant Feature Extraction using Ridgelet and Fourier Transforms. Pattern Analysis and Applications 9(1), 83–93 (2006)
Wang, X.R., Yang, Y.F.: Medical Image Retrieval based on Simplified Multi-Wavelet Transform and Shape Feature. Applied Mechanics and Materials 513, 2871–2875 (2014)
Candès, E.J., Donoho, D.L.: Curvelets - A Surprisingly Effective Non-adaptive Representation for Objects with Edges. In: Rabut, C., Cohen, A., Schumaker, L.L. (eds.) Curves and Surfaces, pp. 105–120. Vanderbilt University Press, Nashville (2000)
Candès, E.J., Donoho, D.L.: Ridgelets: A Key to Higher-dimensional Intermittency? Philosophical Transactions of the Royal Society of London. Series A: Mathematical, Physical and Engineering Sciences 357(1760), 2495–2509 (1999)
Do, M.N., Vetterli, M.: The Contourlet Transform: An Efficient Directional Multi-resolution Image Representation. IEEE Transactions on Image Processing 14(12), 2091–2106 (2005)
Huang, K., Aviyente, S.: Rotation Invariant Texture Classification with Ridgelet Transform and Fourier Transform. In: 2006 IEEE International Conference on Image Processing, pp. 2141–2144 (2006)
Mas Rina, M., Fatimah, A., Ramlan, M., Shyamala, C.D.: Generalized Ridgelet Fourier for M×N Images: Determining the Normalization Criteria. In: Ming, L.S. (ed.) IEEE International Conference on Signal and Image Processing Applications (ICSIPA), Kuala Lumpur, Malaysia, pp. 380–384. IEEE (2009)
Zhu, L.Q., Zhang, S.Y.: Multimodal Biometric Identification System based on Finger Geometry, Knuckle Print, and Palm Print. Pattern Recognition Letters 31(12), 1641–1649 (2010)
Mitchell, H.B.: Image Fusion: Theories, Techniques, and Applications. Springer, Heidelberg (2010)
Latecki, L.J., Lakamper, R., Eckhardt, T.: Shape Descriptors for nNon-rigid Shapes with a Single Closed Contour. In: IEEE Conference on Computer Vision and Pattern Recognition, pp. 424–429. IEEE (2000)
Salton, G., McGill, M.J.: Introduction to Modern Information Retrieval. McGraw-Hill, New York (1983)
Manjunath, B.S., Ohm, J.R., Vasudevan, V.V., Yamada, A.: Color and Texture Descriptors. IEEE Transactions on Circuits and Systems for Video Technology 11(6), 703–715 (2001)
Park, J., An, Y., Jeong, I., Kang, G., Pankoo, K.: Image Indexing using Spatial Multi-resolution Color Correlogram. In: IEEE International Workshop on Imaging Systems and Techniques, pp. 1–4. IEEE (2007)
Mann, P.S.: Introductory Statistics, 7th edn. John Wiley & Sons, Hoboken (2010)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2014 Springer International Publishing Switzerland
About this paper
Cite this paper
Mustaffa, M.R., Ahmad, F., Doraisamy, S. (2014). Multi-resolution Shape-Based Image Retrieval Using Ridgelet Transform. In: Jaafar, A., et al. Information Retrieval Technology. AIRS 2014. Lecture Notes in Computer Science, vol 8870. Springer, Cham. https://doi.org/10.1007/978-3-319-12844-3_10
Download citation
DOI: https://doi.org/10.1007/978-3-319-12844-3_10
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-12843-6
Online ISBN: 978-3-319-12844-3
eBook Packages: Computer ScienceComputer Science (R0)