Semi-supervised discriminative common vector method for computer vision applications
Semi-supervised discriminative common vector method for computer vision applications
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DOI：
10.1016/j.neucom.2013.09.029
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发表时间：
2014-04
期刊：
影响因子：
6
通讯作者：
Hakan Cevikalp
中科院分区：
文献类型：
--
作者：
Hakan Cevikalp
本文提出了一种新的距离度量学习算法，该算法使用了两两相似（等价）约束和不相似约束。该方法适用于许多计算机视觉应用中出现的高维特征空间。它首先将数据投影到与相似样本对的差向量线性张成的正交子空间上。因此，相似的样本具有相同的投影，即每个相似样本对的两个元素之间的距离在投影空间中变为零。在投影空间中，我们找到一个线性嵌入，使不同样本对的散点最大化。这对应于原始输入空间中以正半定矩阵为特征的伪度量。我们还对该方法进行了核化，并表明这允许它处理低维输入空间和大量相似约束的情况。尽管该方法简单，但在综合问题和现实世界图像检索、视觉对象分类、性别分类和图像分割等方面的实验表明，该方法是有效的，比现有的距离度量学习方法有了显著的改进。
We introduce a new algorithm for distance metric learning which uses pairwise similarity (equivalence) and dissimilarity constraints. The method is adapted to the high-dimensional feature spaces that occur in many computer vision applications. It first projects the data onto the subspace orthogonal to the linear span of the difference vectors of similar sample pairs. Similar samples thus have identical projections, i.e., the distance between the two elements of each similar sample pair becomes zero in the projected space. In the projected space we find a linear embedding that maximizes the scatter of the dissimilar sample pairs. This corresponds to a pseudo-metric characterized by a positive semi-definite matrix in the original input space. We also kernelize the method and show that this allows it to handle cases with low-dimensional input spaces and large numbers of similarity constraints. Despite the method's simplicity, experiments on synthetic problems and on real-world image retrieval, visual object classification, gender classification and image segmentation ones demonstrate its effectiveness, yielding significant improvements over the existing distance metric learning methods.