智能系统学报2016，Vol.11Issue(3)：391-400,10.DOI:10.11992/tis.201603028
基于卷积神经网络和哈希编码的图像检索方法
An image retrieval method based on a convolutional neural network and hash coding
摘要
Abstract
For image retrieval, traditional retrieval methods based on artificial features are not effective enough. Hence, we propose an image retrieval method, which combines a convolutional neural network and previous state⁃of⁃the⁃art hash coding strategies. In view of the great progress that convolutional neural networks have made in a large number of computer vision tasks in recent years, this method first uses the model"VGGNet⁃D" pre⁃trained on the ILSVRC′s dataset to extract the convolutional features from experimental image datasets to get the deep repre⁃sentations of images, then adopts previous state⁃of⁃the⁃art hash coding strategies to encode the deep representations to obtain the binary codes, and, finally, performs a quick image retrieval. The experimental results on the common⁃ly used Caltech101 and Caltech256 datasets show that this method′s five strategies, compared with the previous state⁃of⁃the⁃art image retrieval strategies, can obtain better, indeed excellent, performance in both the"Precision⁃Recall" and"mean Average Precision⁃Number of bits" metrics, proving the effectiveness of the proposed method in image retrieval.关键词
图像检索/人工特征/卷积神经网络/卷积特征/哈希编码Key words
image retrieval/artificial features/convolutional neural network/convolutional features/hash coding分类
信息技术与安全科学引用本文复制引用
龚震霆,陈光喜,任夏荔,曹建收..基于卷积神经网络和哈希编码的图像检索方法[J].智能系统学报,2016,11(3):391-400,10.基金项目
国家自然科学基金项目（61462018）；广西学位与研究生教育改革和发展专项课题（ JGY2014060）；广西数字传播与文化软实力中心开放项目（ZFZD1408008）；广西高校图像图形智能处理重点实验室开放基金项目（ LD15042X）． （）
