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	Inception Classification and Object Detection based Joint-CNN for Indoor Scene Classification
	Yanling Tian; Weitong Zhang; Qieshi Zhang; Gang Lu
	2018
会议日期	2018
英文摘要	Abstract—While convolutional neural network (CNN) has been successfully used in many fields including single-label scene classification, it is vital to note that real world scenes generally contain multiple semantics and multi-label, especially in the indoor scene classification due to its content complexity. At the same time, most approaches try to make the network much deeper to make sure that they can extract more detail information. However, the deeper network will cause a lot of problems such as the increase of computational costs and network costs and so on. In order to solve these problems, this paper presents a novel framework which called Joint-CNN based on the proposed special label extraction and network structure. Extensive experiments on various data sets show that our method has enhanced the performance on MIT indoor67 and SUN397 data sets.
内容类型	会议论文
源URL	[http://ir.siat.ac.cn:8080/handle/172644/13780]
专题	深圳先进技术研究院_集成所
推荐引用方式 GB/T 7714	Yanling Tian,Weitong Zhang,Qieshi Zhang,et al. Inception Classification and Object Detection based Joint-CNN for Indoor Scene Classification[C]. 见:. 2018.

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