Multi-annulus partition based image representation for image classification

Authors:
Ye Liang;Jian Yu;Hongzhe Liu;Zhifeng Xiao
Affiliations:
School of Computer and Information Technology, Beijing Jiaotong University, Beijing 100044, China/ College of Information, Beijing Union University, Beijing 100101, China;School of Computer and Information Technology, Beijing Jiaotong University, Beijing 100044, China;Beijing Key Laboratory of Information Service Engineering, Beijing Union University, Beijing 100044, China;Computer Science Department, The University of Alabama, Tuscaloosa, AL 35401, USA
Venue:
International Journal of Sensor Networks
Year:
2013

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Abstract

The paper proposes a new spatial extension of Bag-of-Features BoF formalism for classification tasks. The scheme is based on multi-annulus partition which contains much spatial information of image space. Experiments are conducted using final super-vector image representation in Support Vector Machine SVM framework for classification on Oxford flowers and 15 scenes data sets. The results of experiment have shown the effectiveness of our scheme in terms of multiple performance metrics. In addition, our scheme is conceptually simple and easily adoptable. It can lead to much more compact representations and more invariance to image transformation compared to several existing works.