Dictionary Learning for Stereo Image Representation
Dictionary Learning for Stereo Image Representation
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DOI：
10.1109/tip.2010.2081679
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发表时间：
2011-04
影响因子：
10.6
通讯作者：
I. Tosic;P. Frossard
中科院分区：
文献类型：
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作者：
I. Tosic;P. Frossard
在多视图成像的主要挑战之一是一个表示，揭示了视觉信息的内在几何的定义。具有过完备几何字典的稀疏图像表示提供了一种有效地近似这些图像的方法，使得多视图几何结构在表示中变得显式。然而，在这种情况下，选择一本好词典远非显而易见。我们提出了一种新的方法来学习过完备字典，适用于立体图像的联合表示。我们首先制定了一个稀疏立体图像模型，其中的多视图相关性描述的字典元素（原子）在两个立体视图的局部几何变换。然后提出了一种用于学习立体词典的最大似然（ML）方法，其中概率模型中包含多视图几何约束。ML目标函数使用期望最大化算法进行优化。我们将学习算法应用于全向图像的情况下，在那里我们学习原子的尺度在参数字典。由此产生的字典提供更好的性能，立体全向图像的联合表示，以及改进的多视图特征匹配。最后，我们讨论和展示分布式场景表示和相机姿态估计的字典学习的好处。
One of the major challenges in multi-view imaging is the definition of a representation that reveals the intrinsic geometry of the visual information. Sparse image representations with overcomplete geometric dictionaries offer a way to efficiently approximate these images, such that the multi-view geometric structure becomes explicit in the representation. However, the choice of a good dictionary in this case is far from obvious. We propose a new method for learning overcomplete dictionaries that are adapted to the joint representation of stereo images. We first formulate a sparse stereo image model where the multi-view correlation is described by local geometric transforms of dictionary elements (atoms) in two stereo views. A maximum-likelihood (ML) method for learning stereo dictionaries is then proposed, where a multi-view geometry constraint is included in the probabilistic model. The ML objective function is optimized using the expectation-maximization algorithm. We apply the learning algorithm to the case of omnidirectional images, where we learn scales of atoms in a parametric dictionary. The resulting dictionaries provide better performance in the joint representation of stereo omnidirectional images as well as improved multi-view feature matching. We finally discuss and demonstrate the benefits of dictionary learning for distributed scene representation and camera pose estimation.