Integrated visual vocabulary in latent Dirichlet allocation-based scene classification for IKONOS image

1. A. Bosch, X. Munoz, and R. Marti, “A review: which is the best way to organize/classify image by content,” J. Image Vis. Comput. 25(6), 778–791 (2007), http://dx.doi.org/10.1016/ j.imavis.2006.07.015. 2. D. Larlus and F. Jurie, “Latent mixture vocabularies for object categorization and segmentation,” J. Image Vis. Comput. 27(5), 523–534 (2009), http://dx.doi.org/10.1016/j.imavis .2008.04.022. 3. P. Quelhas et al., “Modelling scenes with local descriptors and latent aspects,” in Proc. IEEE Int. Conf. on Computer Vision, pp. 883–890, IEEE (2005). 4. J. Sivic et al., “Discovering objects and their location in image,” in Proc. IEEE Int. Conf. on Computer Vision, pp. 370–377, IEEE (2005). 5. A. Bosch, A. Zisserman, and X. Munoz, “Scene classification via pLSA,” in Proc. European Conf. on Computer Vision, pp. 517–530, Springer Berlin Heidelberg (2006). 6. L. Fei-Fei and P. Perona, “A Bayesian hierarchical model for learning natural scene categories,” in Proc. IEEE Computer Society Conf. on Computer Vision and Pattern Recognition, pp. 524–531, IEEE (2005). 7. L. Cao and L. Fei-Fei, “Spatially coherent latent topic model for concurrent segmentation and classification of objects and scenes,” in Proc. IEEE Int. Conf. on Computer Vision, pp. 1–8, IEEE (2007). 8. C. Wang, D. Blei, and L. Fei-Fei, “Simultaneous image classification and annotation,” in Proc. IEEE Conf. on Computer Vision and Pattern Recognition, pp. 1903–1910, IEEE (2009). 9. M. Lienou, H. Maitre, and M. Datcu, “Semantic annotation of satellite images using latent Dirichlet allocation,” IEEE Geosci. Remote Sens. Lett. 7(1), 28–32 (2010), http://dx.doi.org/ 10.1109/LGRS.2009.2023536. 10. D. M. Blei, A. Y. Ng, and M. I. Jordan, “Latent Dirichlet allocation,” J. Mach. Learn. Res. 3, 993–1022 (2003). 11. Y. Alqasrawi, D. Neagu, and P. Cowling, “Fusing integrated visual vocabularies-based bag of visual words and weighted color moments on spatial pyramid layout for natural scene image classification,” J. Signal, Image, Video Process. 7(4), 759–775 (2013), http://dx.doi .org/10.1007/s11760-011-0266-0. 12. J. C. van Gemert et al., “Comparing compact codebooks for visual categorization,” J. Comput. Vis. Image Understanding 114(4), 450–462 (2010), http://dx.doi.org/10 .1016/j.cviu.2009.08.004. 13. C. A. Bouman, “CLUSTER: an unsupervised algorithm for modelling Gaussian mixtures,” July 2005, https://engineering.purdue.edu/~bouman/software/cluster/manual.pdf (22 February 2012). 14. G. Heinrich, “Parameter estimation for text analysis,” August 2008, http://faculty.cs.byu .edu/~ringger/CS601R/papers/Heinrich-GibbsLDA.pdf (25 March 2012). 15. T. L. Griffiths and M. Steyvers, “A probabilistic approach to semantic representation,” in Proc. 24th Annual Conf. of the Cognitive Science Society, pp. 381–386 (2002). 16. Y. W. Teh, D. Newman, and M. Welling, “A collapsed variational Bayesian inference algorithm for latent Dirichlet allocation,” in Proc. 20th Annual Conf. on Neural Information Processing Systems, pp. 1353–1360, MIT Press (2006). 17. M. Stricker and M. Orengo, “Similarity of color images,” Proc. SPIE 2420, 381–392 (1995), http://dx.doi.org/10.1117/12.205308. 18. A. Ford and A. Roberts, “Colour space conversion,” 11 August 1998, http://www.poynton .com/PDFs/coloureq.pdf (22 June 2011). 19. P. Pudil, J. Novovicova, and J. Kittler, “Floating search methods in feature selection,” Pattern Recognit. Lett. 15(11), 1119–1125 (1994), http://dx.doi.org/10.1016/0167-8655 (94)90127-9. 20. J. R. Quinlan, C4.5: Programs for Machine Learning, Morgan Kaufmann Publishers Inc, San Francisco (1993). 21. R. Kohavi, “Scalling up the accuracy of naive Bayes classifier: a decision tree hybrid,” in Proc. 2nd Int. Conf. on Knowledge Discovery and Data Mining, pp. 202–207 (1996).