Existing research works in scene image classification have focused on different aspects such as content features (e.g., visual information), context features (e.g., annotations, semantic information, etc.) and both. However, such works suffer from various issues such as higher feature size and lower classification performance. In this paper, we propose a new feature extraction approach for scene image representation using two kinds of rich information: content features and context features. Specifically, the new content features are generated by multi-scale foreground and background information. Similarly, the new context features are generated by the novel compact supervised codebook. Our compact supervised codebook minimizes irrelevant and redundant information, which, in result, achieves the lower-sized contextual feature vector. Finally, we combine both content and context features to represent the scene image. Our experiments on three widely used benchmark scene datasets using Support Vector Machine (SVM) classifier reveal that our proposed context and content features produce better results than existing context and content features, respectively. The fusion of the proposed two types of features significantly outperform numerous state-of-the-art features.

现有的场景图像分类研究工作集中在不同方面，例如内容特征（例如，视觉信息）、上下文特征（例如，注释、语义信息等）以及两者。然而，此类作品存在各种问题，例如更高的特征尺寸和更低的分类性能。在本文中，我们提出了一种使用两种丰富信息的场景图像表示的新特征提取方法：内容特征和上下文特征。具体来说，新的内容特征是由多尺度的前景和背景信息生成的。类似地，新的上下文特征由新颖的紧凑监督码本生成。我们紧凑的监督码本最大限度地减少了不相关和冗余的信息，从而实现了较小尺寸的上下文特征向量。最后，我们结合内容和上下文特征来表示场景图像。我们使用支持向量机 (SVM) 分类器对三个广泛使用的基准场景数据集进行的实验表明，我们提出的上下文和内容特征分别比现有的上下文和内容特征产生更好的结果。所提出的两种特征的融合显着优于许多最先进的特征。