IEEE Trans Image Process. 2013 Dec;22(12):4775-87. doi: 10.1109/TIP.2013.2277825. Epub 2013 Aug 8.
A novel component-level dictionary learning framework that exploits image/video group characteristics based on sparse representation is introduced in this paper. Unlike the previous methods that select the dictionaries to best reconstruct the data, we present an energy minimization formulation that jointly optimizes the learning of both sparse dictionary and component-level importance within one unified framework to provide a discriminative and sparse representation for image/video groups. The importance measures how well each feature component represents the group property with the dictionary. Then, the dictionary is updated iteratively to reduce the influence of unimportant components, thus refining the sparse representation for each group. In the end, by keeping the top K important components, a compact representation is obtained for the sparse coding dictionary. Experimental results on several public image and video data sets are shown to demonstrate the superior performance of the proposed algorithm compared with the-state-of-the-art methods.
本文提出了一种新颖的基于稀疏表示的利用图像/视频组特征的组件级字典学习框架。与之前选择字典以最佳重建数据的方法不同，我们提出了一种能量最小化公式，该公式在一个统一的框架内联合优化稀疏字典和组件级重要性的学习，为图像/视频组提供有判别力和稀疏的表示。重要性度量了每个特征组件用字典表示组属性的好坏程度。然后，字典通过迭代更新来减少不重要组件的影响，从而细化每个组的稀疏表示。最后，通过保留前 K 个重要组件，获得了稀疏编码字典的紧凑表示。在几个公共的图像和视频数据集上的实验结果表明，与最先进的方法相比，所提出的算法具有优越的性能。
