MFC: A Multi-scale Fully Convolutional Approach for Visual Instance Retrieval

CORC > 自动化研究所 > 中国科学院自动化研究所 > 智能感知与计算研究中心

	MFC: A Multi-scale Fully Convolutional Approach for Visual Instance Retrieval
	Hao, Jiedong1,2 ; Wang, Wei1 ; Dong, Jing1 ; Tan, Tieniu1
	2017-09
会议日期	10-14 July 2017
会议地点	Hong Kong
关键词	Visual Instance Retrieval Image Resizing Strategy Multi-scale Representation Fully Convolutional Neural Network
英文摘要	Previous work has shown that feature maps of deep convolutional neural networks (CNNs) can be interpreted as feature representation of an image. Image features aggregated from these feature maps have achieved steady progress in terms of performances on visual instance retrieval tasks in recent years. The key to the success of such methods is feature representation. Inthispaper,westudyhowtorepresentanimage using discriminative features. We demonstrate ﬁrst that image size is an important factor which affects the performance of instance retrieval but has not been thoroughly discussed in previous work. Based on experimental evaluations, we propose a multi-scale fully convolutional (MFC) approach to encode the image efﬁciently and effectively. The proposed method is simple to implement, which does not employ sophisticated post-processing techniques such as query expansion, yet shows promising results on four public datasets.
语种	英语
内容类型	会议论文
源URL	[http://ir.ia.ac.cn/handle/173211/20992]
专题	自动化研究所_智能感知与计算研究中心
通讯作者	Dong, Jing
作者单位	1.中国科学院自动化研究所智能感知与计算研究中心 2.中国科学院大学
推荐引用方式 GB/T 7714	Hao, Jiedong,Wang, Wei,Dong, Jing,et al. MFC: A Multi-scale Fully Convolutional Approach for Visual Instance Retrieval[C]. 见:. Hong Kong. 10-14 July 2017.