Abstract Feature coding is a key component of the bag of visual words (BoVW) model, which is designed to improve image classification and retrieval performance. In the feature coding process, each feature of an image is nonlinearly mapped via a dictionary of visual words to form a high-dimensional sparse vector. Inspired by the well-known locality-constrained linear coding (LLC), we present a locality-constrained affine subspace coding (LASC) method to address the limitation whereby LLC fails to consider the local geometric structure around visual words. LASC is distinguished from all the other coding methods since it constructs a dictionary consisting of an ensemble of affine subspaces. As such, the local geometric structure of a manifold is explicitly modeled by such a dictionary. In the process of coding, each feature is linearly decomposed and weighted to form the first-order LASC vector with respect to its top-k neighboring subspaces. To further boost performance, we propose the second-order LASC vector based on information geometry. We use the proposed coding method to perform both image classification and image retrieval tasks and the experimental results show that the method achieves superior or competitive performance in comparison to state-of-the-art methods.

中文翻译：

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用于图像分类和检索的局部约束仿射子空间编码

摘要 特征编码是视觉词袋（BoVW）模型的关键组成部分，旨在提高图像分类和检索性能。在特征编码过程中，图像的每个特征通过视觉词词典进行非线性映射，形成高维稀疏向量。受众所周知的局部约束线性编码（LLC）的启发，我们提出了一种局部约束仿射子空间编码（LASC）方法来解决 LLC 无法考虑视觉词周围的局部几何结构的局限性。LASC 与所有其他编码方法不同，因为它构建了一个由仿射子空间集合组成的字典。因此，流形的局部几何结构由这样的字典显式建模。在编码过程中，每个特征被线性分解和加权以形成相对于其前 k 个相邻子空间的一阶 LASC 向量。为了进一步提高性能，我们提出了基于信息几何的二阶 LASC 向量。我们使用所提出的编码方法来执行图像分类和图像检索任务，实验结果表明，与最先进的方法相比，该方法实现了优越或有竞争力的性能。