Paper Info
Title
Object class recognition using SIFT and Bayesian networks
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.
Year
DOI
Venue
2010
10.1007/978-3-642-16773-7_5
MICAI (2)
Keywords
Field
DocType
poor performance,object class recognition task,classification task,classification step,bayesian network approach,sift feature,partial occlusion,object identification,layer bayesian network,object class recognition,bayesian network
Computer vision,Scale-invariant feature transform,Object class recognition,3D single-object recognition,Pattern recognition,Computer science,Robustness (computer science),Bayesian network,Artificial intelligence,Invariant (mathematics),Cluster analysis,Machine learning
Conference
Volume
ISSN
ISBN
6438
0302-9743
3-642-16772-1
Citations 
PageRank 
References 
2
0.37
8
Authors
4
Name
Order
Citations
PageRank
Leonardo Chang14211.15
Miriam Monica Duarte220.37
L. Enrique Sucar31016118.72
Eduardo F. Morales455957.67