Toyota Technological Institute in Chicago, 6045 S. Kenwood Ave., Chicago, IL 60637, USA.
IEEE Trans Pattern Anal Mach Intell. 2012 Oct;34(10):1978-91. doi: 10.1109/TPAMI.2011.276.
Typically, object recognition is performed based solely on the appearance of the object. However, relevant information also exists in the scene surrounding the object. In this paper, we explore the roles that appearance and contextual information play in object recognition. Through machine experiments and human studies, we show that the importance of contextual information varies with the quality of the appearance information, such as an image's resolution. Our machine experiments explicitly model context between object categories through the use of relative location and relative scale, in addition to co-occurrence. With the use of our context model, our algorithm achieves state-of-the-art performance on the MSRC and Corel data sets. We perform recognition tests for machines and human subjects on low and high resolution images, which vary significantly in the amount of appearance information present, using just the object appearance information, the combination of appearance and context, as well as just context without object appearance information (blind recognition). We also explore the impact of the different sources of context (co-occurrence, relative-location, and relative-scale). We find that the importance of different types of contextual information varies significantly across data sets such as MSRC and PASCAL.
通常，物体识别仅基于物体的外观来进行。然而，物体周围的场景中也存在相关信息。在本文中，我们探讨了外观和上下文信息在物体识别中所扮演的角色。通过机器实验和人类研究，我们表明上下文信息的重要性随着外观信息的质量（例如图像的分辨率）而变化。我们的机器实验通过使用相对位置和相对比例以及共现来明确地对物体类别之间的上下文进行建模。通过使用我们的上下文模型，我们的算法在 MSRC 和 Corel 数据集上实现了最先进的性能。我们对机器和人类受试者进行了低分辨率和高分辨率图像的识别测试，这些图像在呈现的外观信息量方面存在显著差异，仅使用物体外观信息、外观和上下文的组合，以及没有物体外观信息的上下文（盲目识别）。我们还探讨了不同上下文来源（共现、相对位置和相对比例）的影响。我们发现，不同类型的上下文信息的重要性在 MSRC 和 PASCAL 等数据集之间存在显著差异。
