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

• Type: Journal
• Year: 2010
• Where: MICAI
• Authors: Leonardo Chang, Miriam Monica Duarte, Luis Enrique Sucar, Eduardo F. Morales
• Keywords: Artificial Intelligence, Layer Bayesian Network, MICAI 2010, Object Class Recognition, Partial Occlusion