Torralba Antonio, Oliva Aude
Artificial Intelligence Laboratory, MIT, Cambridge, MA 02139, USA.
Network. 2003 Aug;14(3):391-412.
In this paper we study the statistical properties of natural images belonging to different categories and their relevance for scene and object categorization tasks. We discuss how second-order statistics are correlated with image categories, scene scale and objects. We propose how scene categorization could be computed in a feedforward manner in order to provide top-down and contextual information very early in the visual processing chain. Results show how visual categorization based directly on low-level features, without grouping or segmentation stages, can benefit object localization and identification. We show how simple image statistics can be used to predict the presence and absence of objects in the scene before exploring the image.
在本文中，我们研究了属于不同类别的自然图像的统计特性及其与场景和物体分类任务的相关性。我们讨论了二阶统计量如何与图像类别、场景尺度和物体相关联。我们提出了如何以前馈方式计算场景分类，以便在视觉处理链的早期就提供自上而下和上下文信息。结果表明，直接基于低级特征进行视觉分类，无需分组或分割阶段，如何能够有利于物体定位和识别。我们展示了在探索图像之前，简单的图像统计量如何能够用于预测场景中物体的存在与否。
