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MICAI
2010
Springer

Object Class Recognition Using SIFT and Bayesian Networks

8 years 2 months ago
Object Class Recognition Using SIFT and Bayesian Networks
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
Leonardo Chang, Miriam Monica Duarte, Luis Enrique
Added 29 Jan 2011
Updated 29 Jan 2011
Type Journal
Year 2010
Where MICAI
Authors Leonardo Chang, Miriam Monica Duarte, Luis Enrique Sucar, Eduardo F. Morales
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