CVPR Indoor Scene Recognition

![]() Indoor scene recognition is a challenging open problem in high level vision. Most scene recognition models that work well for outdoor scenes perform poorly in the indoor domain. The main difficulty is that while some indoor scenes (e.g. corridors) can be well characterized by global spatial properties, others (e.g., bookstores) are better characterized by the objects they contain. More generally, to address the indoor scenes recognition problem we need a model that can exploit local and global discriminative information. ##Database The database contains 67 Indoor categories, and a total of 15620 images. The number of images varies across categories, but there are at least 100 images per category. All images are in jpg format. The images provided here are for research purposes only. ##Evaluation For the results in the paper we use a subset of the dataset that has the same number of training and testing samples per class. The partition

室内场景识别是高级视觉领域中的一个极具挑战性的开放性问题。大多数在室外场景中表现良好的场景识别模型在室内领域表现欠佳。其主要难点在于，尽管某些室内场景（例如走廊）可以通过全局空间属性得到良好描述，而另一些场景（例如书店）则更适宜通过其所包含的物体来定义。更普遍而言，为了解决室内场景识别问题，我们需要一个能够利用局部和全局判别信息的模型。##数据库 本数据库包含67个室内类别，总计15620张图像。各类别图像数量不尽相同，但每个类别至少有100张图像。所有图像均为jpg格式。提供的图像仅供研究之用。##评估 论文中的结果使用数据集的一个子集，其中每个类别的训练样本和测试样本数量相同。