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首页> 外文期刊>Pattern Recognition: The Journal of the Pattern Recognition Society >Learning pairwise image similarities for multi-classification using Kernel Regression Trees
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Learning pairwise image similarities for multi-classification using Kernel Regression Trees

机译:使用核回归树学习成对图像相似性以进行多分类

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摘要

We are often faced with the problem of distinguishing between visually similar objects that share the same general appearance characteristics. As opposed to object categorization, this task is focused on capturing fine image differences in a pose-dependent fashion. Our research addresses this particular family of problems and is centered around the concept of learning from example pairs. Formally, we construct a parameterized visual similarity function optimally separating the pairs of images that depict the objects of the same class or identity from the pairs representing different object classes/identities. It combines various image distances that are quantified by comparing local descriptor responses at the corresponding locations in both paired images. To find the best combinations, we train ensembles of so-called Kernel Regression Trees which model the desired similarity function as a hierarchy of fuzzy decision stumps. The obtained function is then used within a k-NN-like framework to address complex multi-classification problems. Through the experiments with several image datasets we demonstrate the numerous advantages of the proposed approach: high classification accuracy, scalability, ease of interpretation and the independence of the feature representation.
机译:我们经常面临区分具有相同总体外观特征的视觉相似对象的问题。与对象分类相反,此任务专注于以姿势相关的方式捕获精细的图像差异。我们的研究解决了这一特殊的问题家族,并以从示例对中学习的概念为中心。形式上,我们构造了一个参数化的视觉相似度函数,该函数将代表相同类别或身份的对象的图像对与代表不同对象类别/身份的对之间进行了最佳分离。它组合了各种图像距离,这些距离通过比较两个配对图像中相应位置处的局部描述符响应来量化。为了找到最佳组合,我们训练了所谓的内核回归树的合奏,该树将所需的相似性函数建模为模糊决策树桩的层次结构。然后,在类似k-NN的框架内使用获得的函数来解决复杂的多分类问题。通过对几个图像数据集的实验,我们证明了该方法的众多优势:分类精度高,可伸缩性,易于解释以及特征表示的独立性。

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