Abstract
A unique way in which content based image retrieval (CBIR) for remote sensing differs widely from traditional CBIR is the widespread occurrences of weak textures. The task of representing the weak textures becomes even more challenging especially if image properties like scale, illumination or the viewing geometry are not known.
In this work, we have proposed the use of a new feature ‘texton histogram’ to capture the weak-textured nature of remote sensing images. Combined with an automatic classifier, our texton histograms are robust to variations in scale, orientation and illumination conditions as illustrated experimentally. The classification accuracy is further improved using additional image driven features obtained by the application of a feature selection procedure.
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Kitamoto, A.: Digital typhoon: Near real-time aggregation, recombination and delivery of typhoon-related information. In: Fourth International Symposium on Digital Earth (CD–ROM) (2005)
Puzicha, J., Hofmann, T., Buhmann, J.: Non-parametric similarity measures for unsupervised texture segmentation and image retrieval. In: Computer Vision and Pattern Recognition (CVPR 1997), Washington, DC, USA, p. 267. IEEE Computer Society, Los Alamitos (1997)
Newsam, S., Wang, L., Bhagavathy, S., Manjunath, B.S.: Using texture to annotate remote sensed datasets. In: 3rd International Symposium on Image and Signal Processing and Analysis (ISPA) (2003)
Newsam, S., Wang, L., Bhagavathy, S., Manjunath, B.S.: Using texture to analyze and manage large collections of remote sensed image and video data. Journal of Applied Optics: Information Processing 43, 210–217 (2004)
Wang, L., Liu, J.: Texture classification using multiresolution markov random field models. Pattern Recogn. Lett. 20, 171–182 (1999)
Valeriano, M.I., Escada, S.: Mining patterns of change in remote sensing image databases. In: Fifth IEEE International Conference on Data Mining (ICDM 2005), pp. 362–369 (2005)
Schroder, M., Rehrauer, H., Seidel, K., Datcu, M.: Spatial information retrieval from remote- sensing images - part 2: Gibbs-markov random fields. IEEE Trans. Geosci. Remote Sensing, 1446–1455 (1998)
Unser, M.: Texture classification and segmentation using wavelet frames. Image Processing, IEEE Transactions on 4, 1549–1560 (1995)
Wang, J.Z., Wiederhold, G., Firschein, O., Wei, S.X.: Content-based image indexing and searching using daubechies’ wavelets. International Journal on Digital Libraries 1, 311–328 (1997)
Wang, J., Li, J., Wiederhold, G.: SIMPLIcity: Semantics-sensitive integrated matching for picture LIbraries. IEEE Transactions on Pattern Analysis and Machine Intelligence 23, 947–963 (2001)
Varma, M., Zisserman, A.: Classifying images of materials: Achieving viewpoint and illumination independence. In: Heyden, A., Sparr, G., Nielsen, M., Johansen, P. (eds.) ECCV 2002. LNCS, vol. 2352, pp. 255–271. Springer, Heidelberg (2002)
Varma, M., Zisserman, A.: Statistical approaches to material classification. In: Second Indian Conference on Computer Vision, Graphics and Image Processing, pp. 167–172 (2002)
Varma, M., Zisserman, A.: Texture classification: Are filter banks necessary? In: International Conference on Computer Vision and Pattern recognition, pp. 691–698 (2003)
Julesz, B.: Textons, the elements of texture perception, and their interactions. Nature 290, 91–97 (1981)
Malik, J., Belongie, S., Shi, J., Leung, T.K.: Textons, contours and regions: Cue integration in image segmentation. In: ICCV (2), pp. 918–925 (1999)
Gilles, S.: Robust description and matching of images. Technical report, University of Oxford, Ph.D. Thesis (1998)
Yang, C., Lozano-Perez, T.: Image database retrieval with multiple-instance learning techniques. In: Proc. International Conference on Data Engineering, pp. 233–243 (2000)
Quinlan, J.R.: Induction of decision trees. In: Shavlik, J., Dietterich, T. (eds.) Readings in Machine Learning., Morgan Kaufmann, San Francisco (1990); Originally published in Machine Learning 1, 81–106 (1986)
John, G.H., Kohavi, R., Pfleger, K.: Irrelevant features and the subset selection problem. In: International Conference on Machine Learning, pp. 121–129 (1994)
Vogel, J., Schiele, B.: Natural scene retrieval based on a semantic modeling step. In: Enser, P.G.B., Kompatsiaris, Y., O’Connor, N.E., Smeaton, A.F., Smeulders, A.W.M. (eds.) CIVR 2004. LNCS, vol. 3115, Springer, Heidelberg (2004)
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Sawant, N., Chandran, S., Mohan, B.K. (2006). Retrieving Images for Remote Sensing Applications. In: Kalra, P.K., Peleg, S. (eds) Computer Vision, Graphics and Image Processing. Lecture Notes in Computer Science, vol 4338. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11949619_76
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DOI: https://doi.org/10.1007/11949619_76
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-68301-8
Online ISBN: 978-3-540-68302-5
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