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Similarity Measures of Satellite Images Using an Adaptive Feature Contrast Model

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DOI: 10.4236/ijg.2013.42031    3,091 Downloads   4,747 Views   Citations

ABSTRACT

Similarity measurement is one of key operations to retrieve “desired” images from an image database. As a famous psychological similarity measure approach, the Feature Contrast (FC) model is defined as a linear combination of both common and distinct features. In this paper, an adaptive feature contrast (AdaFC) model is proposed to measure similarity between satellite images for image retrieval. In the AdaFC, an adaptive function is used to model a variable role of distinct features in the similarity measurement. Specifically, given some distinct features in a satellite image, e.g., a COAST image, they might play a significant role when the image is compared with an image including different semantics, e.g., a SEA image, and might be trivial when it is compared with a third image including same semantics, e.g., another COAST image. Experimental results on satellite images show that the proposed model can consistently improve similarity retrieval effectiveness of satellite images including multiple geo-objects, for example COAST images.

Conflicts of Interest

The authors declare no conflicts of interest.

Cite this paper

H. Tang, A. Gong, S. Li, W. Yi and C. Yang, "Similarity Measures of Satellite Images Using an Adaptive Feature Contrast Model," International Journal of Geosciences, Vol. 4 No. 2, 2013, pp. 329-343. doi: 10.4236/ijg.2013.42031.

References

[1] M. Datcu, K. Seidel and M. Walessa, “Spatial Information Retrieval from Remote-Sensing Images. I. Information Theoretical Perspective,” IEEE Transactions on Geoscience and Remote Sensing, Vol. 36, No. 5, 1998, pp. 1431-1445. doi:10.1109/36.718847
[2] M. Datcu, H. Daschiel, A. Pelizzari, M. Quartulli, A. Galoppo, A. Colapicchioni, M. Pastori, K. Seidel, P. G. Marchetti and S. D’Elia, “Information Mining in Remote Sensing Image Archives: System Concepts,” IEEE Transactions on Geoscience and Remote Sensing, Vol. 41, No. 12, 2003, pp. 2923-2936. doi:10.1109/TGRS.2003.817197
[3] B. S. Manjunath, J. R. Ohm, V. V. Vasudevan and A. Yamada, “Color and Texture Descriptors,” IEEE Transactions On Circuits and Systems for Video Technology, Vol. 11, No. 6, 2001, pp. 703-715. doi:10.1109/76.927424
[4] J. Li and J. Wang, “IRM: Integrated Region Matching for Image Retrieval,” Proceedings ACM Multimedia, Los Angeles, 30 October-3 November 2000, pp. 147-156.
[5] K. Barnard, P. Duygulu and D. Forsyth, “Matching Words and Pictures,” Journal of Machine Learning Research, Vol. 3, No. 2, 2007, pp. 1107-1135.
[6] G. Carneiro, A. B. Chan, P. J. Moreno and N. Vasconcelos, “Supervised Learning of Semantic Classes for Image Annotation and Retrieval,” IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol. 29, No. 3, 2007, pp. 394-410. doi:10.1109/TPAMI.2007.61
[7] A. Mojsilovic, J. Gomes and B. Rogowitz, “Semantic-Friendly Indexing and Quering of Images Based on the Extraction of the Objective Semantic Cues,” International Journal of Computer Vision, Vol. 56, No. 1-2, 2004, pp. 79-107. doi:10.1023/B:VISI.0000004833.39906.33
[8] J. Vogel and B. Schiele, “Semantic Modeling of Natural Scenes for Content-Based Image Retrieval,” International Journal of Computer Vision, Vol. 72, No. 2, 2007, pp. 133-157. doi:10.1007/s11263-006-8614-1
[9] J. Z. Wang, J. Li and G. Wiederhold, “SIMPLIcity: Semantics-Sensitive Integrated Matching for Picture Libraries,” IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol. 23, No. 9, 2001, pp. 947-963. doi:10.1109/34.955109
[10] M. Ferecatu, N. Boujemaa and M. Crucianu, “Semantic Interactive Image Retrieval Combining Visual and Conceptual Content Description,” Multimedia Systems, Vol. 13, No. 5-6, 2008, pp. 309-322. doi:10.1007/s00530-007-0094-9
[11] X. F. He, O. King and W. Y. Ma, “Learning a Semantic Space from User’s Relevance Feedback for Image Retrieval,” IEEE Transactions on Circuits and Systems for Video Technology, Vol. 13, No. 1, 2003, pp. 39-48. doi:10.1109/TCSVT.2002.808087
[12] S. Santini, A. Gupta and R. Jain, “Emergent Semantics through Interaction in Image Databases,” IEEE Transactions on Knowledge and Data Engineering, Vol. 13, No. 3, 2001, pp. 337-351. doi:10.1109/69.929893
[13] C. Zhang and T. S. Chen, “An Active Learning Framework for Content-Based Information Retrieval,” IEEE Transactions on Multimedia, Vol. 4, No. 2, 2002, pp. 260-268. doi:10.1109/TMM.2002.1017738
[14] E. Horster, “Topic Models for Image Retrieval on Large-Scale Databases,” Dissertation, University of Augsburg, 2009.
[15] A. Tversky, “Features of Similarity,” Psychological Review, Vol. 84, No. 4, 1977, pp. 327-352. doi:10.1037/0033-295X.84.4.327
[16] S. Santini and R. Jain, “Similarity Measures,” IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol. 21, No. 9, 1999, pp. 871-883. doi:10.1109/34.790428
[17] H. Tang, T. Fang, P. J. Du and P. F. Shi, “Intra-Dimensional Feature Diagnosticity in the Fuzzy Feature Contrast Model,” Image and Vision Computing, Vol. 26, No. 6, 2008, pp. 751-760. doi:10.1016/j.imavis.2007.08.009
[18] Y. Chen and J. Z. Wang, “A Region-Based Fuzzy Feature Matching Approach to Content-Based Image Retrieval,” IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol. 24, No. 9, 2002, pp. 1252-1267. doi:10.1109/TPAMI.2002.1033216
[19] Y. Chen and J. Z. Wang, “Image Categorization by Learning and Reasoning with Regions,” Journal of Machine Learning Research, Vol. 5, 2004, pp. 913-939.
[20] S. Lazebnik, C. Schmid and J. Ponce, “Beyond Bags of Features: Spatial Pyramid Matching for Recognizing Natural Scene Categories,” IEEE Computer Society Conference on Computer Vision and Pattern Recognition, Vol. 2, 2006, pp. 2169-2178.
[21] F. F. Li and P. Pietro, “A Bayesian Hierarchical Model for Learning Natural Scene Categories,” IEEE Computer Society Conference on Computer Vision and Pattern Recognition, Vol. 2, 2005, pp. 524-531.
[22] P. Quelhas, F. Monay and J. M. Odobez, “Modeling Scenes with Local Descriptors and Latent Aspects,” IEEE International Conference on Computer Vision, Vol. 1, 2005, pp. 883-890.
[23] A. Bosch, X. Munoz and R. Marti, “Which Is the Best Way to Organize/Classify Image by Content,” Image and Vision Computing, Vol. 25, No. 3, 2007, pp. 778-791. doi:10.1016/j.imavis.2006.07.015
[24] G. Csurka, C. Dance, L. Fan, J. Williamowski and C. Bray, “Visual Categorization with Bags of Keypoints,” ECCV Workshop on Statistical Learning in Computer Vision, Prague, 2004.
[25] R. L. Goldstone, “Similarity,” In: R. Wilson and F. C. Keil, Eds., MIT Encyclopedia of the Cognitive Sciences, MIT Press, Cambridge, 1999, pp. 763-765.
[26] R. N. Shepard, “Toward a Universal Law of Generalization for Psychological Science,” Science, Vol. 237, No. 4820, 1987, pp. 1317-1323. doi:10.1126/science.3629243
[27] R. L. Goldstone, “Alignment-Based Nonmonotonicities in Similarity,” Journal of Experimental Psychology: Learning, Memory, and Cognition, Vol. 22, No. 4, 1996, pp. 988-1001. doi:10.1037/0278-7393.22.4.988
[28] U. Hahna, N. Chater and L. B. Richardson, “Similarity as Transformation,” Cognition, Vol. 87, No. 1, 2003, pp. 1-32. doi:10.1016/S0010-0277(02)00184-1
[29] J. N. Daniel and M. D. Lee, “Common and Distinctive Features in Stimulus Similarity: A Modified Version of the Contrast Model,” Psychologic Bulletin & Review, Vol. 11, No. 6, 2004, pp. 961-974. doi:10.3758/BF03196728
[30] D. M. Blei, A. Y. Ng and M. I. Jordan, “Latent Dirichlet Allocation,” Journal of Machine Learning Research, Vol. 3, No. 2, 2003, pp. 993-1022.
[31] T. Hofmann, “Unsupervised Learning by Probabilistic Latent Semantic Analysis,” Machine Learning, Vol. 42, No. 1-2, 2001, pp. 177-196.
[32] R. Lienhart and M. Slaney, “pLSA on Large Scale Image Databases,” IEEE International Conference on Acoustics, Speech and Signal Processing, Honolulu, 15-20 April 2007, pp. 1217-1220.
[33] E. Horster, R. Lienhart and M. Slaney, “Image Retrieval on Large-Scale Image Databases,” ACM International Conference on Image and Video Retrieval (CIVR), Amsterdam, 2007, pp. 17-24.
[34] B. S. Manjunath and W. Y. Ma, “Texture Features for Browsing and Retrieval of Image Data.,” IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol. 18, No. 8, 1996, pp. 837-842. doi:10.1109/34.531803
[35] Y. Deng and B. S. Manjunath, “Unsupervised Segmentation of Color-Texture Regions in Images and Video,” IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol. 23, No. 8, 2001, pp. 800-810. doi:10.1109/34.946985

  
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