Abstract
The classification of raw data often involves the problem of selecting the appropriate set of features to represent the input data. In general, various features can be extracted from the input dataset, but only some of them are actually relevant for the classification process. Since relevant features are often unknown in real-world problems, many candidate features are usually introduced. This degrades both the speed and the predictive accuracy of the classifier due to the presence of redundancy in the candidate feature set.
In this paper, we study the capability of a special class of motifs previously introduced in the literature, i.e. 2D irredundant motifs, when they are exploited as features for image classification. In particular, such a class of motifs showed to be powerful in capturing the relevant information of digital images, also achieving good performances for image compression. We embed such 2D feature motifs in a bag-of-words model, and then exploit K-nearest neighbour for the classification step. Preliminary results obtained on both a benchmark image dataset and a video frames dataset are promising.
Keywords
- Visual Word
- Training Image
- Scale Invariant Feature Transform
- Target Concept
- Probabilistic Latent Semantic Analysis
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.
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References
Amelio, A., Apostolico, A., Rombo, S.E.: Image compression by 2D motif basis. In: Data Compression Conference (DCC 2011), pp. 153–162 (2011)
Apostolico, A., Parida, L.: Incremental paradigms of motif discovery. J. of Comp. Biol. 11(1), 15–25 (2004)
Apostolico, A., Parida, L., Rombo, S.E.: Motif patterns in 2D. Theoretical Computer Science 390(1), 40–55 (2008)
Bay, H., Ess, A., Tuytelaars, T., Van Gool, L.: Speeded-Up Robust Features (SURF). Computer Vision and Image Understanding 110(3), 346–359 (2008)
Berg, A.C., Berg, T.L., Malik, J.: Shape matching and object recognition using low distortion correspondences. In: Proc. of IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR 2005), vol. 1, pp. 26–33 (2005)
Bosch, A., Muñoz, X., Martí, R.: Review: Which is the best way to organize/classify images by content? Image Vision Comput. 25(6), 778–791 (2007)
Bosch, A., Zisserman, A., Muñoz, X.: Scene classification using a hybrid generative/discriminative approach. IEEE Trans. Pattern Anal. Mach. Intell. 30(4), 712–727 (2008)
Dash, M., Liu, H.: Feature selection for classification. Intelligent Data Analysis 1, 131–156 (1997)
Keogh, E.J., et al.: Supporting exact indexing of arbitrarily rotated shapes and periodic time series under euclidean and warping distance measures. VLDB J. 18(3), 611–630 (2009)
Fredriksson, K., Mäkinen, V., Navarro, G.: Rotation and lighting invariant template matching. Information and Computation 205(7), 1096–1113 (2007)
Grossi, R., Pisanti, N., Crochemore, M., Sagot, M.-F.: Bases of motifs for generating repeated patterns with wild cards. IEEE/ACM Trans. Comp. Biol. Bioinf. 2(3), 159–177 (2000)
Hofmann, T.: Unsupervised learning by probabilistic latent semantic analysis. Machine Learning 42(1-2), 177–196 (2001)
Hundt, C., Liskiewicz, M., Nevries, R.: A combinatorial geometrical approach to two-dimensional robust pattern matching with scaling and rotation. Theoretical Computer Science 410(51), 5317–5333 (2009)
John, G.H., Kohavi, R., Pfleger, K.: Irrelevant features and the subset selection problem. In: Machine Learning: Proceedings of the Eleventh International, pp. 121–129 (1994)
Kadir, T., Brady, M.: Saliency, scale and image description. Int. J. Comput. Vision 45(2), 83–105 (2001)
Lowe, D.G.: Object recognition from local scale-invariant features. In: Proc. of the 7th IEEE International Conference on Computer Vision, vol. 2, pp. 1150–1157 (1999)
Lowe, D.G.: Local feature view clustering for 3D object recognition. In: Proc. of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR 2001), pp. 682–688 (2001)
Lu, D., Weng, Q.: A survey of image classification methods and techniques for improving classification performance. International Journal of Remote Sensing 28(5), 823–870 (2007)
Marée, R., Geurts, P., Piater, J.H., Wehenkel, L.: Biomedical image classification with random subwindows and decision trees. In: Liu, Y., Jiang, T.-Z., Zhang, C. (eds.) CVBIA 2005. LNCS, vol. 3765, pp. 220–229. Springer, Heidelberg (2005)
Marée, R., Geurts, P., Piater, J., Wehenkel, L.: Random subwindows for robust image classification. In: Proc. of International Conference on Computer Vision and Pattern Recognition (CVPR), pp. 34–40 (2005)
Matas, J., Obdrzálek, S.: Object recognition methods based on transformation covariant features. In: 12th European Signal Processing Conference (2004)
Nanni, L., Lumini, A., Brahnam, S.: Survey on LBP based texture descriptors for image classification. Expert Syst. Appl. 39(3), 3634–3641 (2012)
Parida, L., Pizzi, C., Rombo, S.E.: Characterization and extraction of irredundant tandem motifs. In: Calderón-Benavides, L., González-Caro, C., Chávez, E., Ziviani, N. (eds.) SPIRE 2012. LNCS, vol. 7608, pp. 385–397. Springer, Heidelberg (2012)
Philbin, J., Chum, O., Isard, M., Sivic, J., Zisserman, A.: Object retrieval with large vocabularies and fast spatial matching. In: IEEE Conference on Computer Vision and Pattern Recognition (CVPR 2007), pp. 1–8 (2007)
Rombo, S.E.: Optimal extraction of motif patterns in 2D. Information Processing Letters 109(17), 1015–1020 (2009)
Rombo, S.E.: Extracting string motif bases for quorum higher than two. Theor. Comput. Sci. 460, 94–103 (2012)
Rombo, S.E., Terracina, G.: Discovering representative models in large time series databases. In: Christiansen, H., Hacid, M.-S., Andreasen, T., Larsen, H.L. (eds.) FQAS 2004. LNCS (LNAI), vol. 3055, pp. 84–97. Springer, Heidelberg (2004)
Shao, H., Svoboda, T., Ferrari, V., Tuytelaars, T., Van Gool, L.: Fast indexing for image retrieval based on local appearance with re-ranking. In: Proc. of International Conference on Image Processing (ICIP 2003), vol. 2, pp. III-737–III740 (2003)
Shao, H., Svoboda, T., Van Gool, L.: Zubud - Zurich building database for image based recognition. Technical Report TR-260, Computer Vision Lab, Swiss Federal Institute of Technology, Switzerland (2003)
Shao, H., Svoboda, T., Tuytelaars, T., Van Gool, L.: HPAT indexing for fast object/scene recognition based on local appearance. In: Bakker, E.M., Lew, M., Huang, T.S., Sebe, N., Zhou, X.S. (eds.) CIVR 2003. LNCS, vol. 2728, pp. 71–80. Springer, Heidelberg (2003)
Xie, N., Ling, H., Hu, W., Zhang, X.: Use bin-ratio information for category and scene classification. In: IEEE Conference on Computer Vision and Pattern Recognition (CVPR 2010), pp. 2313–2319 (2010)
Yang, J., Jiang, Y.-G., Hauptmann, A.G., Ngo, C.-W.: Evaluating bag-of-visual-words representations in scene classification. In: Proceedings of the International Workshop on Workshop on Multimedia Information Retrieval, MIR 2007, pp. 197–206 (2007)
Zhou, L., Zhou, Z., Hu, D.: Scene classification using a multi-resolution bag-of-features model. Pattern Recognition 46(1), 424–433 (2013)
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Furfaro, A., Groccia, M.C., Rombo, S.E. (2013). Image Classification Based on 2D Feature Motifs. In: Larsen, H.L., Martin-Bautista, M.J., Vila, M.A., Andreasen, T., Christiansen, H. (eds) Flexible Query Answering Systems. FQAS 2013. Lecture Notes in Computer Science(), vol 8132. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-40769-7_30
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DOI: https://doi.org/10.1007/978-3-642-40769-7_30
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