Title: Multi-level K-means clustering and group sparse coding with quasi-SIFT feature for image classification

Authors: Zhang Lihe; Ma Chen

Addresses: School of Information and Communication Engineering, Dalian University of Technology, Liaoning 116023, China ' School of Information and Communication Engineering, Dalian University of Technology, Liaoning 116023, China

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.

Keywords: image classification; group sparse coding; k-means clustering; quasi-SIFT features; SIFT; scale-invariant feature transform.

DOI: 10.1504/IJICT.2015.070326

International Journal of Information and Communication Technology, 2015 Vol.7 No.4/5, pp.495 - 507

Received: 15 Mar 2014
Accepted: 12 Jun 2014
Published online: 02 Jul 2015 *

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