Abstract
Several methods have been presented in the literature that successfully used SIFT features for object identification, as they are reasonably invariant to translation, rotation, scale, illumination and partial occlusion. However, they have poor performance for classification tasks. In this work, SIFT features are used to solve problems of object class recognition in images using a two-step process. In its first step, the proposed method performs clustering on the extracted features in order to characterize the appearance of classes. Then, in the classification step, it uses a three layer Bayesian network for object class recognition. Experiments show quantitatively that clusters of SIFT features are suitable to represent classes of objects. The main contributions of this paper are the introduction of a Bayesian network approach in the classification step to improve performance in an object class recognition task, and a detailed experimentation that shows robustness to changes in illumination, scale, rotation and partial occlusion.
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Chang, L., Duarte, M.M., Sucar, L.E., Morales, E.F. (2010). Object Class Recognition Using SIFT and Bayesian Networks. In: Sidorov, G., Hernández Aguirre, A., Reyes García, C.A. (eds) Advances in Soft Computing. MICAI 2010. Lecture Notes in Computer Science(), vol 6438. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-16773-7_5
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DOI: https://doi.org/10.1007/978-3-642-16773-7_5
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-16772-0
Online ISBN: 978-3-642-16773-7
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