Image Retrieval Based on CNN Visual Features

doi:10.13190/j.jbupt.2015.s1.023

JOURNAL OF BEIJING UNIVERSITY OF POSTS AND TELECOM ›› 2015, Vol. 38 ›› Issue (s1): 103-106,120.doi: 10.13190/j.jbupt.2015.s1.023

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Image Retrieval Based on CNN Visual Features

LI Zhao^1,2,3, LU Wei¹, XING Wei-wei¹, SUN Zhan-quan^2,3, WANG Wei-dong¹, WEI Yun-chao¹

1. School of Software Engineering, Beijing Jiaotong University, Beijing 100044, China;
2. National Supercomputing Center in Jinan, Shandong Computer Science Center, Jinan 250014, China;
3. Shandong Provincial Key Laboratory of Computer Network, Jinan 250014, China

Received:2014-08-22 Online:2015-06-28 Published:2015-06-28

Abstract

Abstract:

Convolutional neural network (CNN) currently becomes research focus for image recognition. The visual features extracted from the pre-trained CNN demonstrate powerful ability for various recognition tasks. The performance of traditional visual features and CNN visual features for content-based image retrieval was mainly compared. Experiments on the two public available datasets of Pascal Sentence and Pascal VOC 2007 show that, a sufficient performance of CNN visual features used in image retrieval when compared with traditional visual features.

Key words: convolutional neural network, content-based image retrieval, feature extraction, deep learning

CLC Number:

TN911.22

LI Zhao, LU Wei, XING Wei-wei, SUN Zhan-quan, WANG Wei-dong, WEI Yun-chao. Image Retrieval Based on CNN Visual Features[J]. JOURNAL OF BEIJING UNIVERSITY OF POSTS AND TELECOM, 2015, 38(s1): 103-106,120.

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Image Retrieval Based on CNN Visual Features

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