A Bayesian approach for object classification based on clusters of SIFT local features

Authors:
Leonardo Chang;Miriam M. Duarte;L. E. Sucar;Eduardo F. Morales
Affiliations:
Advanced Technologies Application Center, 7th Avenue, Number 21812, Siboney, Playa, Havana, Cuba and National Institute for Astrophysics, Optics and Electronics, Luis Enrique Erro No. 1, Sta. Mar& ...;National Institute for Astrophysics, Optics and Electronics, Luis Enrique Erro No. 1, Sta. María Tonanzintla, Puebla, México C.P. 72840, Mexico;National Institute for Astrophysics, Optics and Electronics, Luis Enrique Erro No. 1, Sta. María Tonanzintla, Puebla, México C.P. 72840, Mexico;National Institute for Astrophysics, Optics and Electronics, Luis Enrique Erro No. 1, Sta. María Tonanzintla, Puebla, México C.P. 72840, Mexico
Venue:
Expert Systems with Applications: An International Journal
Year:
2012

Citing 15
Cited 1

Algorithms for clustering data

Algorithms for clustering data
Parallel algorithms for hierarchical clustering

Parallel Computing
Saliency, Scale and Image Description

International Journal of Computer Vision
Introduction to Bayesian Networks

Introduction to Bayesian Networks
Scale & Affine Invariant Interest Point Detectors

International Journal of Computer Vision
Distinctive Image Features from Scale-Invariant Keypoints

International Journal of Computer Vision
Learning to Detect Objects in Images via a Sparse, Part-Based Representation

IEEE Transactions on Pattern Analysis and Machine Intelligence
Pedestrian Detection in Crowded Scenes

CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Volume 1 - Volume 01
A Bayesian Hierarchical Model for Learning Natural Scene Categories

CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Volume 2 - Volume 02
A Performance Evaluation of Local Descriptors

IEEE Transactions on Pattern Analysis and Machine Intelligence
A Maximum Entropy Framework for Part-Based Texture and Object Recognition

ICCV '05 Proceedings of the Tenth IEEE International Conference on Computer Vision (ICCV'05) Volume 1 - Volume 01
Local Features for Object Class Recognition

ICCV '05 Proceedings of the Tenth IEEE International Conference on Computer Vision - Volume 2
Local Features and Kernels for Classification of Texture and Object Categories: A Comprehensive Study

International Journal of Computer Vision
Speeded-Up Robust Features (SURF)

Computer Vision and Image Understanding
Local invariant feature detectors: a survey

Foundations and Trends® in Computer Graphics and Vision

An indication of unification for different clustering approaches

Pattern Recognition

Quantified Score

Hi-index	12.05

Visualization

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 object class recognition problems 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 the different 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.