Image set recognition has been widely applied in many practical problems like real-time video retrieval and image caption tasks. Due to its superior performance, it has grown into a significant topic in recent years. However, images with complicated variations, e.g., postures and human ages, are difficult to address, as these variations are continuous and gradual with respect to image appearance. Consequently, the crucial point of image set recognition is to mine the intrinsic connection or structural information from the image batches with variations. In this work, a Discriminant Residual Analysis (DRA) method is proposed to improve the classification performance by discovering discriminant features in related and unrelated groups. Specifically, DRA attempts to obtain a powerful projection which casts the residual representations into a discriminant subspace. Such a projection subspace is expected to magnify the useful information of the input space as much as possible, then the relation between the training set and the test set described by the given metric or distance will be more precise in the discriminant subspace. We also propose a nonfeasance strategy by defining another approach to construct the unrelated groups, which help to reduce furthermore the cost of sampling errors. Two regularization approaches are used to deal with the probable small sample size problem. Extensive experiments are conducted on benchmark databases, and the results show superiority and efficiency of the new methods.

中文翻译：

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具有姿势和年龄变化的图像集分类的判别残差分析。


图像集识别已广泛应用于实时视频检索和图像字幕任务等许多实际问题。由于其优越的性能，近年来它已成为一个重要的话题。然而，具有复杂变化的图像，例如姿势和人类年龄，很难处理，因为这些变化就图像外观而言是连续的和渐进的。因此，图像集识别的关键是从具有变化的图像批次中挖掘内在联系或结构信息。在这项工作中，提出了一种判别残差分析（DRA）方法，通过发现相关和不相关组中的判别特征来提高分类性能。具体来说，DRA 尝试获得强大的投影，将残差表示投射到判别子空间中。这样的投影子空间期望尽可能放大输入空间的有用信息，那么给定度量或距离描述的训练集和测试集之间的关系在判别子空间中将更加精确。我们还通过定义另一种构建不相关组的方法来提出不作为策略，这有助于进一步减少抽样错误的成本。使用两种正则化方法来处理可能的小样本量问题。在基准数据库上进行了大量的实验，结果表明了新方法的优越性和效率。