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	TWO-STAGE POOLING OF DEEP CONVOLUTIONAL FEATURES FOR IMAGE RETRIEVAL
	Zhi, Tiancheng ; Duan, Ling-Yu ; Wang, Yitong ; Huang, Tiejun
	2016
关键词	image representation image retrieval convolutional neural network feature pooling compact descriptor
英文摘要	Convolutional Neural Network (CNN) based image representations have achieved high performance in image retrieval tasks. However, traditional CNN based global representations either provide high dimensional features, which incurs large memory consumption and computing cost, or inadequately capture discriminative information in images, which degenerates the functionality of CNN features. To address those issues, we propose a two-stage partial mean pooling (PMP) approach to construct compact and discriminative global feature representations. The proposed PMP is meant to tackle the limits of traditional max pooling and mean (or average) pooling. By injecting the PMP pooling strategy into the CNN based patch-level mid-level feature extraction and representation, we have significantly improved the state-of-the-art retrieval performance over several common benchmark datasets.; CPCI-S(ISTP); tiancheng.zhi@pku.edu.cn; lingyu@pku.edu.cn; wangyitong@pku.edu.cn; tjhuang@pku.edu.cn; 2465-2469
语种	英语
出处	23rd IEEE International Conference on Image Processing (ICIP)
内容类型	其他
源URL	[http://ir.pku.edu.cn/handle/20.500.11897/459881]
专题	信息科学技术学院
推荐引用方式 GB/T 7714	Zhi, Tiancheng,Duan, Ling-Yu,Wang, Yitong,et al. TWO-STAGE POOLING OF DEEP CONVOLUTIONAL FEATURES FOR IMAGE RETRIEVAL. 2016-01-01.

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