Multi-level K-means clustering and group sparse coding with quasi-SIFT feature for image classification Online publication date: Thu, 02-Jul-2015
by Zhang Lihe; Ma Chen
International Journal of Information and Communication Technology (IJICT), Vol. 7, No. 4/5, 2015
Abstract: Traditional sparse coding treats local descriptors separately, leading to random locations of non-zero coefficients. To overcome this problem, group sparse coding was proposed. By leveraging mixed-norm regularisation, the locations of non-zero coefficients tend to cluster into groups. However, how to rationally constitute 'a group' is still a problem, since forcing local descriptors in a group to have uniform sparse pattern may be too rigid and bring about larger reconstruction error and information loss, especially when descriptors in a group are not very similar. In this paper, we propose multi-level K-means clustering and group sparse coding to address this issue, which is called MK-GSC. Moreover, we present a novel local descriptor based on SIFT to further promote the power of representation for images. Our method is tested on two public benchmarks, and achieves competitive or better results than the state-of-the-art methods.
Existing subscribers:
Go to Inderscience Online Journals to access the Full Text of this article.
If you are not a subscriber and you just want to read the full contents of this article, buy online access here.Complimentary Subscribers, Editors or Members of the Editorial Board of the International Journal of Information and Communication Technology (IJICT):
Login with your Inderscience username and password:
Want to subscribe?
A subscription gives you complete access to all articles in the current issue, as well as to all articles in the previous three years (where applicable). See our Orders page to subscribe.
If you still need assistance, please email subs@inderscience.com
Our Blog 
Follow us on Twitter 
Visit us on Facebook