论文“混合表情的稀疏表达分析”正式出版

宋彩风,刘伟锋,王延江. 混合表情的稀疏表达分析[J]. 计算机工程与应用, 2013, 49(23): 122-126.

摘要 提出了一种混合表情的定量描述方法。基于压缩感知的理论框架，以面部特征点的Gabor小波系数为表情特征对混合表情进行了分析；利用隶属度函数定量表示混合表情中的不同组成。实验结果表明，该方法可以简单有效地表示混合表情中各基本表情的组成。

关键词： 表情分析   压缩感知   混合表情   Gabor   隶属度   截集

http://cea.ceaj.org/CN/abstract/abstract31261.shtml

CVIU paper is online.

Please cite this article as: W. Liu, D. Tao, J. Cheng,  Y. Tang, Multiview Hessian Discriminative Sparse Coding for

Image Annotation, Computer Vision and Image Understanding, 118:50-60, (2013), doi:  http://dx.doi.org/10.1016/j.cviu.2013.03.007

Abstract

Sparse coding represents a signal sparsely by using an overcomplete dictionary, and obtains promising performance in practical computer vision applications, especially for signal restoration tasks such as image denoising and image inpainting. In recent years, many discriminative sparse coding algorithms have been developed for classification problems, but they cannot naturally handle visual data represented by multiview features. In addition, existing sparse coding algorithms use graph Laplacian to model the local geometry of the data distribution. It has been identified that Laplacian regularization biases the solution towards a constant function which possibly leads to poor extrapolating power. In this paper, we present multiview Hessian discriminative sparse coding (mHDSC) which seamlessly integrates Hessian regularization with discriminative sparse coding for multiview learning problems. In particular, mHDSC exploits Hessian regularization to steer the solution which varies smoothly along geodesics in the manifold, and treats the label information as an additional view of feature for incorporating the discriminative power for image annotation. We conduct extensive experiments on PASCAL VOC'07 dataset and demonstrate the effectiveness of mHDSC for image annotation.

Keywords: Image annotation; Hessian; multiview; sparse coding

CVIU paper is online now.

Please cite this article as: W. Liu, D. Tao, J. Cheng,  Y. Tang, Multiview Hessian Discriminative Sparse Coding for Image Annotation, Computer Vision and Image Understanding (2013), doi:  http://dx.doi.org/10.1016/j.cviu.2013.03.007

Abstract

Sparse coding represents a signal sparsely by using an overcomplete dictionary, and obtains promising performance in practical computer vision applications, especially for signal restoration tasks such as image denoising and image inpainting. In recent years, many discriminative sparse coding algorithms have been developed for classification problems, but they cannot naturally handle visual data represented by multiview features. In addition, existing sparse coding algorithms use graph Laplacian to model the local geometry of the data distribution. It has been identified that Laplacian regularization biases the solution towards a constant function which possibly leads to poor extrapolating power. In this paper, we present multiview Hessian discriminative sparse coding (mHDSC) which seamlessly integrates Hessian regularization with discriminative sparse coding for multiview learning problems. In particular, mHDSC exploits Hessian regularization to steer the solution which varies smoothly along geodesics in the manifold, and treats the label information as an additional view of feature for incorporating the discriminative power for image annotation. We conduct extensive experiments on PASCAL VOC'07 dataset and demonstrate the effectiveness of mHDSC for image annotation.

Keywords: Image annotation; Hessian; multiview; sparse coding