基于卷积神经网络和流形排序的图像检索算法

doi:10.11772/j.issn.1001-9081.2016.02.0531

计算机应用 ›› 2016, Vol. 36 ›› Issue (2): 531-534.DOI: 10.11772/j.issn.1001-9081.2016.02.0531

基于卷积神经网络和流形排序的图像检索算法

刘兵^1,2, 张鸿^1,2

1. 武汉科技大学计算机科学与技术学院, 武汉 430065;
2. 武汉科技大学智能信息处理与实时工业系统湖北省重点实验室, 武汉 430065

收稿日期:2015-07-24 修回日期:2015-09-12 出版日期:2016-02-10 发布日期:2016-02-03
通讯作者: 张鸿(1979-),女,湖北襄阳人,教授,博士,CCF会员,主要研究方向:跨媒体检索、机器学习、数据挖掘。
作者简介:刘兵(1992-),男,湖北广水人,硕士研究生,主要研究方向:深度学习、图像处理。
基金资助:
国家自然科学基金资助项目(61003127,61373109)。

Image retrieval algorithm based on convolutional neural network and manifold ranking

LIU Bing^1,2, ZHANG Hong^1,2

1. School of Computer Science and Technology, Wuhan University of Science and Technology, Wuhan Hubei 430065, China;
2. Hubei Province Key Laboratory of Intelligent Information Processing and Real-Time Industrial System, Wuhan University of Science and Technology, Wuhan Hubei 430065, China

Received:2015-07-24 Revised:2015-09-12 Online:2016-02-10 Published:2016-02-03

摘要/Abstract

摘要： 针对基于内容的图像检索(CBIR)中低层视觉特征与用户对图像理解的高层语义不一致以及传统的距离度量方式难以真实反映图像之间相似程度等问题,提出了一种基于卷积神经网络(CNN)和流形排序的图像检索算法。首先,将图像输入CNN,通过多层神经网络对图像的监督学习,提取网络中全连接层的图像特征;其次,对图像特征进行归一化处理,然后用高效流形排序(EMR)算法对查询图像所返回的结果进行排序;最后,根据排序的结果返回最相似的图像。在corel数据集上,深度图像特征比基于场景描述的图像特征的平均查准率(mAP)提高了53.74%,流形排序比余弦距离度量方式的mAP提高了18.34%。实验结果表明,所提算法能够有效地提高图像检索的准确率。

关键词: 图像检索, 深度学习, 卷积神经网络, 特征提取, 流形排序

Abstract: In Content-Based Image Retrieval (CBIR), the low-level visual features are not consistent with the high-level semantic features captured by human, and it is difficult to reflect the similarity of images by traditional distance measurements. To solve these problems, an image retrieval algorithm based on Convolutional Neural Network (CNN) and manifold ranking was proposed. Firstly, the image dataset was put into CNN, image features were extracted through the fully connected layers of the network after supervised learning; secondly, the image features were normalized and then Efficient Manifold Ranking (EMR) algorithm was used to return the ranked scores for query images; finally, the most similar images were returned to users according to the scores. In corel dataset, the mean Average Precision (mAP) of deep image feature was 53.74% higher than that of the scene descriptor features, and the mAP of efficient manifold ranking was 18.34% higher than that of the cosine distance. The experimental results show that the proposed algorithm can effectively improve the accuracy of image retrieval.

Key words: image retrieval, deep learning, Convolutional Neural Network(CNN), feature extraction, manifold ranking

中图分类号:

TP391.413

刘兵, 张鸿. 基于卷积神经网络和流形排序的图像检索算法[J]. 计算机应用, 2016, 36(2): 531-534.

LIU Bing, ZHANG Hong. Image retrieval algorithm based on convolutional neural network and manifold ranking[J]. Journal of Computer Applications, 2016, 36(2): 531-534.

参考文献

[1] WU L, HOI S C H, YU N. Semantics-preserving bag-of-words models and applications[J]. IEEE Transactions on Image Processing, 2010, 19(7): 1908-1920.
[2] YANG J, JIANG Y G, HAUPTMANN A G, et al. Evaluating bag-of-visual-words representations in scene classification[C]//Proceedings of the 2007 International Workshop on Workshop on Multimedia Information Retrieval. New York: ACM, 2007: 197-206.
[3] LOWE D G. Object recognition from local scale-invariant features[C]//Proceedings of the 1999 IEEE International Conference on Computer Vision. Piscataway, NJ: IEEE, 1999: 1150-1157.
[4] BAY H, ESS A, TUYTELAARS T, et al. Speeded-Up Robust Features (SURF)[J]. Computer Vision and Image Understanding, 2008, 110(3): 346-359.
[5] SCHMIDHUBER J. Deep learning in neural networks: an overview[J]. Neural Networks, 2015, 61: 85-117.
[6] WAN J, WANG D, HOI S C H, et al. Deep learning for content-based image retrieval: a comprehensive study[C]//Proceedings of the 2014 ACM International Conference on Multimedia. New York: ACM, 2014: 157-166.
[7] WU P, HOI S C H, XIA H, et al. Online multimodal deep similarity learning with application to image retrieval[C]//Proceedings of the 21st ACM International Conference on Multimedia. New York: ACM, 2013: 153-162.
[8] KRIZHEYSKY A, SUTSKEVER I, HINTON G E. ImageNet classification with deep convolutional neural networks[C]//Proceedings of the 26th Annual Conference on Neural Information Processing Systems. Lake Tahoe, Nevada: [s.n.], 2012: 1097-1105.
[9] VEDALDI A, LENC K. MatConvNet — convolutional neural networks for MATLAB [EB/OL]. [2015-06-21]. http://arxiv.org/pdf/1412.4564.pdf.
[10] CHATFIELD K, SIMONYAN K, VEDALDI A, et al. Return of the devil in the details: delving deep into convolutional nets [EB/OL]. [2014-11-05]. http://arxiv.org/pdf/1405.3531.pdf
[11] BABENKO A, SLESAREV A, CHIGORIN A, et al. Neural codes for image retrieval[C]//ECCV 2014: Proceedings of the 13th European Conference on Computer Vision. Berlin: Springer, 2014: 584-599.
[12] DONAHUE J, JIA Y, VINYALS O, et al. DeCAF: a deep convolutional activation feature for generic visual recognition [EB/OL]. [2015-05-10]. http://arxiv.org/abs/1310.1531v1.
[13] ZHOU D, WESTON J, GRETTON A, et al. Ranking on data manifolds[C]//NIPS 2003: Proceedings of the 2003 Advances in Neural Information Processing Systems. Cambridge, MA: MIT Press, 2004, 16: 169-176.
[14] XU B, BU J, CHEN C, et al. Efficient manifold ranking for image retrieval[C]//Proceedings of the 34th International ACM SIGIR Conference on Research and Development in Information Retrieval. New York: ACM, 2011: 525-534.
[15] OLIVA A, TORRALBA A. Modeling the shape of the scene: a holistic representation of the spatial envelope[J]. International Journal of Computer Vision, 2001, 42(3): 145-175.
[16] CHECHIK G, SHARMA V, SHALIT U, et al. Large scale online learning of image similarity through ranking[J]. Journal of Machine Learning Research, 2009, 11(2): 1109-1135.

基于卷积神经网络和流形排序的图像检索算法

Image retrieval algorithm based on convolutional neural network and manifold ranking

PDF

可视化

摘要/Abstract

引用本文

使用本文

参考文献

相关文章 15

编辑推荐

Metrics

[1]	陈成瑞, 孙宁, 何世彪, 廖勇. 面向C-V2X通信的基于深度学习的联合信道估计与均衡算法[J]. 计算机应用, 2021, 41(9): 2687-2693.
[2]	宋中山, 梁家锐, 郑禄, 刘振宇, 帖军. 基于双向门控尺度特征融合的遥感场景分类[J]. 计算机应用, 2021, 41(9): 2726-2735.
[3]	李康康, 张静. 基于注意力机制的多层次编码和解码的图像描述模型[J]. 计算机应用, 2021, 41(9): 2504-2509.
[4]	张永斌, 常文欣, 孙连山, 张航. 基于字典的域名生成算法生成域名的检测方法[J]. 计算机应用, 2021, 41(9): 2609-2614.
[5]	赵宏, 孔东一. 图像特征注意力与自适应注意力融合的图像内容中文描述[J]. 计算机应用, 2021, 41(9): 2496-2503.
[6]	徐江浪, 李林燕, 万新军, 胡伏原. 结合目标检测的室内场景识别方法[J]. 计算机应用, 2021, 41(9): 2720-2725.
[7]	牟长宁, 王海鹏, 周丕宇, 侯鑫行. 基于图卷积神经网络的串联质谱从头测序[J]. 计算机应用, 2021, 41(9): 2773-2779.
[8]	谢德峰, 吉建民. 融入句法感知表示进行句法增强的语义解析[J]. 计算机应用, 2021, 41(9): 2489-2495.
[9]	代雨柔, 杨庆, 张凤荔, 周帆. 基于自监督学习的社交网络用户轨迹预测模型[J]. 计算机应用, 2021, 41(9): 2545-2551.
[10]	王贺兵, 张春梅. 基于非对称卷积-压缩激发-次代残差网络的人脸关键点检测[J]. 计算机应用, 2021, 41(9): 2741-2747.
[11]	郑志强, 胡鑫, 翁智, 王雨禾, 程曦. 基于改进DenseNet的牛眼图像特征提取方法[J]. 计算机应用, 2021, 41(9): 2780-2784.
[12]	曹玉红, 徐海, 刘荪傲, 王紫霄, 李宏亮. 基于深度学习的医学影像分割研究综述[J]. 计算机应用, 2021, 41(8): 2273-2287.
[13]	秦斌斌, 彭良康, 卢向明, 钱江波. 司机分心驾驶检测研究进展[J]. 计算机应用, 2021, 41(8): 2330-2337.
[14]	黄程程, 董霄霄, 李钊. 基于二维Winograd算法的深流水线5×5卷积方法[J]. 计算机应用, 2021, 41(8): 2258-2264.
[15]	曾祥银, 郑伯川, 刘丹. 基于深度卷积神经网络和聚类的左右轨道线检测[J]. 计算机应用, 2021, 41(8): 2324-2329.