Abstract
The problem of content-based retrieval of remotely sensed images presents a major challenge not only because of the surprisingly increasing volume of images acquired from a wide range of sensors but also because of the complexity of images themselves. In this paper, a software system for content-based retrieval of remote sensing images, using spatiograms is introduced. In addition, we also compare our results with histogram based content retrieval. Finally we illustrate the effect and relation of quantization bins on the retrieval efficiency of histogram & spatiogram based content retrieval system. Bhattacharyya coefficient is obtained in order to make comparisons between histogram & spatiogram of two images. Experimental results show that the integration of spatial information in histogram improves the image analysis of remote sensing data and the proposed method is simple, accurate and costs much less time than the traditional ones.
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References
Linda, G.S., George, C.S.: Computer Vision. Prentice-Hall, Englewood Cliffs (2001)
Nilsson, M., Bartunek, J.S., Nordberg, J., Claesson, I.: On Histograms and Spatiograms - Introduction of the Mapogram. In: ICIP, pp. 973–976 (2008)
Birchfield, S.T., Rangarajan, S.: Spatiograms versus Histograms for Region-Based Tracking. In: CVPR 2005 (2005)
Linda, G.S., George, C.S.: Computer Vision. Prentice-Hall, Englewood Cliffs (2003)
Stricker, M., Swain, M.: The Capacity of Color Histogram Indexing, Computer Vision and Pattern Recognition. In: Proceedings of IEEE Computer Society Conference on CVPR, pp. 704–708 (1994)
Adrian, U., Christoph, L., Daniel, K.: Spatiogram-Based Shot Distances for Video Retrieval
Djouadi, A., Snorrason, O., Garber, F.: The quality of Training-Sample estimates of the Bhattacharyya coefficient. IEEE Transactions on Pattern Analysis and Machine Intelligence, 92–97 (1990)
Venteres, C.C., Cooper, M.: A Review of Content-Based Image Retrieval Systems
Shengjiu, W.: A Robust CBIR Approach Using Local Color Histograms. Department of Computer Science, University of Alberta, Edmonton, Alberta, Canada, Tech. Rep. TR 01-13 (October 2001)
Bjorn, J.: QBIC (Query By Image Content), http://www.isy.liu.se/cvl/Projects/VISIT-bjojo/survey/surveyonCBIR/node26.html
Manjunath, B.S.: Color and texture descriptors. IEEE Trans. CSVT 11(6), 703–715 (2001)
Stanchev, P.L., Green Jr., D., Dimitrov, B.: High level color similarity retrieval. Int. J. Inf. Theories Appl., 363–369 (2003)
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Singh, B.K., Sinha, G.R., Khan, I. (2010). Comparison of Histogram and Spatiograms for Content Based Retrieval of Remote Sensing Images. In: Das, V.V., et al. Information Processing and Management. BAIP 2010. Communications in Computer and Information Science, vol 70. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-12214-9_26
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DOI: https://doi.org/10.1007/978-3-642-12214-9_26
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-12213-2
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