Spatial locality-aware sparse coding and dictionary learning
Date of Issue2012
Asian Conference on Machine Learning, ACML (4th : 2012)
School of Electrical and Electronic Engineering
Nonlinear encoding of SIFT features has recently shown good promise in image classification. This scheme is able to reduce the training complexity of the traditional bag-of-feature approaches while achieving better performance. As a result, it is suitable for large-scale image classification applications. However, existing nonlinear encoding methods do not explicitly consider the spatial relationship when encoding the local features, but merely leaving the spatial information used at a later stage, e.g. through the spatial pyramid matching, is largely inadequate. In this paper, we propose a joint sparse coding and dictionary learning scheme that take the spatial information into consideration in encoding. Our experiments on synthetic data and benchmark data demonstrate that the proposed scheme can learn a better dictionary and achieve higher classification accuracy.
DRNTU::Engineering::Electrical and electronic engineering
© 2012 The Authors (Journal of Machine Learning Research). This paper was published in Journal of Machine Learning Research and is made available as an electronic reprint (preprint) with permission of The Authors (Journal of Machine Learning Research). The paper can be found at the following official URL: [http://jmlr.org/proceedings/papers/v25/wang12a/wang12a.pdf]. One print or electronic copy may be made for personal use only. Systematic or multiple reproduction, distribution to multiple locations via electronic or other means, duplication of any material in this paper for a fee or for commercial purposes, or modification of the content of the paper is prohibited and is subject to penalties under law.