Abstract
This paper proposes a method to recognize scene categories using bags of visual words obtained hierarchically partitioning into subregion the input images. Specifically, for each subregion the Textons distribution and the extension of the corresponding subregion are taken into account. The bags of visual words computed on the subregions are weighted and used to represent the whole scene. The classification of scenes is carried out by a Support Vector Machine. A k-nearest neighbor algorithm and a similarity measure based on Bhattacharyya coefficient are used to retrieve from the scene database those that contain similar visual content to a given a scene used as query. Experimental tests using fifteen different scene categories show that the proposed approach achieves good performances with respect to the state of the art methods.
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References
Torralba, A.: Contextual priming for object detection. International Journal of Computer Vision 53(2), 169–191 (2003)
Vogel, J., Schiele, B.: Semantic modeling of natural scenes for content-based image retrieval. International Journal of Computer Vision 72(2), 133–157 (2007)
Battiato, S., Farinella, G.M., Giuffrida, G., Sismeiro, C., Tribulato, G.: Using visual and text features for direct marketing on multimedia messaging services domain. Multimedia Tools and Applications Journal(in press, 2008)
Bosch, A., Zisserman, A., Munoz, X.: Scene classification via pLSA. In: Leonardis, A., Bischof, H., Pinz, A. (eds.) ECCV 2006. LNCS, vol. 3954, pp. 517–530. Springer, Heidelberg (2006)
Fei-Fei, L., Perona, P.: A hierarchical bayesian model for learning natural scene categories. In: IEEE Computer Science Society International Conference of Computer Vision and Pattern Recognition (CVPR), San Diego, CA, USA (June 2005)
Renninger, L.W., Malik, J.: When is scene recognition just texture recognition? Vision Research 44, 2301–2311 (2004)
Ladret, P., Guérin-Dugué, A.: Categorisation and retrieval of scene photographs from jpeg compressed database. Pattern Analysis & Application 4, 185–199 (2001)
Oliva, A., Torralba, A.: Modeling the shape of the scene: a holistic representation of the spatial envelope. International Journal of Computer Vision 42, 145–175 (2001)
Lazebnik, S., Schmid, C., Ponce, J.: Beyond bags of features: Spatial pyramid matching for recognizing natural scene categories. In: IEEE Conference on Computer Vision and Pattern Recognition, vol. II, pp. 2169–2178 (2006)
Matthew, R.B., Jiebo, L.: Beyond pixels: Exploiting camera metadata for photo classification. Pattern Recognition 38(6), 935–946 (2005)
Sivic, J., Zisserman, A.: Video Google: A text retrieval approach to object matching in videos. In: Proceedings of the International Conference on Computer Vision, October 2003, vol. 2, pp. 1470–1477 (2003)
Julesz, B.: Textons, the elements of texture perception, and their interactions. Nature 290, 91–97 (1981)
Varma, M., Zisserman, A.: A statistical approach to texture classification from single images. International Journal of Computer Vision 62(1–2), 61–81 (2005)
Shawe-Taylor, J., Cristianini, N.: Support Vector Machines and other kernel-based learning methods. Cambridge University Press, Cambridge (2000)
Comaniciu, D., Ramesh, V., Meer, P.: Kernel-based object tracking. IEEE Trans. Pattern Anal. Mach. Intell. 25(5), 564–575 (2003)
Dance, C., Willamowski, J., Fan, L., Bray, C., Csurka, G.: Visual categorization with bags of keypoints. In: ECCV International Workshop on Statistical Learning in Computer Vision (2004)
Shotton, J., Johnson, J., Cipolla, M.,, R.: Semantic texton forests for image categorization and segmentation. In: IEEE Computer Science Society International Conference of Computer Vision and Pattern Recognition, CVPR (2008)
Gonzalez, R.C., Woods, R.E.: Digital Image Processing, 3rd edn. Prentice-Hall, Inc, Upper Saddle River (2006)
Chang, C.C., Lin, C.J.: LIBSVM: a library for support vector machines (2001)
Battiato, S., Farinella, G.M., Gallo, G., Ravì, D.: Scene categorization using bag of textons on spatial hierarchy. In: IEEE International Conference on Image Processing - ICIP 2008 (2008)
Farinella, G.M., Battiato, S., Gallo, G., Cipolla, R.: Natural Versus Artificial Scene Classification by Ordering Discrete Fourier Power Spectra. In: Proceedings of 12th International Workshop on Structural and Syntactic Pattern Recognition (SSPR)- Satellite event of the 19th International Conference of Pattern Recognition (ICPR). LNCS. Springer, Heidelberg (2008)
Battiato, S., Farinella, G.M., Gallo, G., Messina, E.: Classification of compressed images in constrained application domains. In: SPIE-IS&T 21th Annual Symposium Electronic Imaging Science and Technology 2009 - Digital Photography V (2009)
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Battiato, S., Farinella, G.M., Gallo, G., Ravì, D. (2009). Spatial Hierarchy of Textons Distributions for Scene Classification. In: Huet, B., Smeaton, A., Mayer-Patel, K., Avrithis, Y. (eds) Advances in Multimedia Modeling . MMM 2009. Lecture Notes in Computer Science, vol 5371. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-92892-8_35
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DOI: https://doi.org/10.1007/978-3-540-92892-8_35
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