Abstract
The main difficulty in the binary object classification field lays in dealing with a high variability of symbol appearance. Rotation, partial occlusions, elastic deformations, or intra-class and inter-class variabilities are just a few problems. In this paper, we introduce a novel object description for this type of symbols. The shape of the object is aligned based on principal components to make the recognition invariant to rotation and reflection. We propose the Blurred Shape Model (BSM) to describe the binary objects. This descriptor encodes the probability of appearance of the pixels that outline the object’s shape. Besides, we present the use of this descriptor in a system to improve the BSM performance and deal with binary objects multi-classification problems. Adaboost is used to train the binary classifiers, learning the BSM features that better split object classes. Then, the different binary problems learned by the Adaboost are embedded in the Error Correcting Output Codes framework (ECOC) to deal with the muti-class case. The methodology is evaluated in a wide set of object classes from the MPEG07 repository. Different state-of-the-art descriptors are compared, showing the robustness and better performance of the proposed scheme when classifying objects with high variability of appearance.
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Torralba, A., Murphy, K., Freeman, W.: Sharing visual features for multiclass and multiview object detection, Technical Report, Massachusetts Institute of Technology Computer Science and Artificial Intelligence (MIT AIM) (2004)
Friedman, J., Hastie, T., Tibshirani, R.: Additive logistic regression: a statistical view of boosting. The Annals of Statistics 8(2), 337–374 (1998)
Pujol, O., Radeva, P., Vitrià, J.: Discriminant ECOC: a heuristic method for application dependent design of error correcting output codes. IEEE Transaction on Pattern Analysis and Machine Intelligence 28, 1007–1012 (2006)
Zhang, D., Lu, G.: Review of shape representation and description techniques. Pattern Recognition 37, 1–19 (2004)
Kim, W.: A new region-based shape descriptor, Technical report, Hanyang University and Konan Technology (1999)
ISO/IEC 15938-5:2003(E)
Lladós, J., Valveny, E., Sánchez, G., Martí, E.: Symbol Recognition: Current Advances and Perspectives. In: Blostein, D., Kwon, Y.-B. (eds.) GREC 2001. LNCS, vol. 2390, pp. 104–127. Springer, Heidelberg (2002)
Manjunath, B., Salembier, P., Sikora, T.: Introduction to mpeg-7, Multimedia content description interface. John Wiley and Sons, Chichester (2002)
Escalera, S., Pujol, O., Radeva, P.: Decoding of Ternary Error Correcting Output Codes. In: Martínez-Trinidad, J.F., Carrasco Ochoa, J.A., Kittler, J. (eds.) CIARP 2006. LNCS, vol. 4225, Springer, Heidelberg (2006)
Dietterich, T., Bakiri, G.: Solving multiclass learning problems via error-correcting output codes. Artificial Intelligence Research 2, 263–286 (1995)
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Escalera, S., Fornès, A., Pujol, O., Lladós, J., Radeva, P. (2007). Multi-class Binary Object Categorization Using Blurred Shape Models. In: Rueda, L., Mery, D., Kittler, J. (eds) Progress in Pattern Recognition, Image Analysis and Applications. CIARP 2007. Lecture Notes in Computer Science, vol 4756. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-76725-1_16
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DOI: https://doi.org/10.1007/978-3-540-76725-1_16
Publisher Name: Springer, Berlin, Heidelberg
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