论文标题
场景图像表示的内容和上下文功能
Content and Context Features for Scene Image Representation
论文作者
论文摘要
场景图像分类中的现有研究集中在内容功能（例如，视觉信息）或上下文特征（例如注释）上。当他们捕获有关图像的不同信息，这些信息可以互补且可用于区分不同类别的图像时，我们认为它们的融合将改善分类结果。在本文中，我们提出了新技术来计算内容功能和上下文功能，然后将它们融合在一起。对于内容功能，我们根据图像中的背景和前景信息设计多尺度的深度功能。对于上下文功能，我们使用网络中可用的类似图像的注释来设计过滤单词（代码簿）。我们使用支持向量机分类器的三个广泛使用基准场景数据集中的实验表明，我们提出的上下文和内容功能分别比现有上下文和内容功能更好。拟议的两种功能的融合显着超过了众多最先进的功能。
Existing research in scene image classification has focused on either content features (e.g., visual information) or context features (e.g., annotations). As they capture different information about images which can be complementary and useful to discriminate images of different classes, we suppose the fusion of them will improve classification results. In this paper, we propose new techniques to compute content features and context features, and then fuse them together. For content features, we design multi-scale deep features based on background and foreground information in images. For context features, we use annotations of similar images available in the web to design a filter words (codebook). Our experiments in three widely used benchmark scene datasets using support vector machine 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.
