GONG Zhenting,CHEN Guangxi,REN Xiali,et al.An image retrieval method based on a convolutional neural network and hash coding[J].CAAI Transactions on Intelligent Systems,2016,11(3):391-400.[doi:10.11992/tis.201603028]





An image retrieval method based on a convolutional neural network and hash coding
龚震霆12 陈光喜12 任夏荔12 曹建收12
1. 桂林电子科技大学 计算机与信息安全学院, 广西 桂林 541004;
2. 广西高校图像图形智能处理重点实验室, 广西 桂林 541004
GONG Zhenting12 CHEN Guangxi12 REN Xiali12 CAO Jianshou12
1. School of Computer and Information Security, Guilin University of Electronic Technology, Guilin 541004, China;
2. Guangxi Colleges and Universities Key Laboratory of Intelligent Processing of Computer Images and Graphics, Guilin 541004, China
image retrievalartificial featuresconvolutional neural networkconvolutional featureshash coding
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 representations 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 commonly 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.


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更新日期/Last Update: 1900-01-01