浏览全部资源
扫码关注微信
Received：14 June 2019，
Revised：2019-9-28，
Accepted：05 October 2019，
Published：16 May 2020
移动端阅览
目的
2
为提取可充分表达图像语义信息的图像特征，减少哈希检索中的投影误差，并生成更紧致的二值哈希码，提出一种基于密集网络和改进的监督核哈希方法。
方法
2
用训练优化好的密集网络提取图像的高层语义特征；先对提取到的图像特征进行核主成分分析投影，充分挖掘图像特征中隐含的非线性信息，以减少投影误差，再利用监督核哈希方法对图像特征进行监督学习，将特征映射到汉明空间，生成更紧致的二值哈希码。
结果
2
为验证提出方法的有效性、可拓展性以及高效性，在Paris6K和LUNA16（lung nodule analysis 16）数据集上与其他6种常用哈希方法相比，所提方法在不同哈希码长下的平均检索精度均较高，且在哈希码长为64 bit时，平均检索精度达到最高，分别为89.2%和92.9%；与基于卷积神经网络的哈希算法（convolution neural network Hashing，CNNH）方法相比，所提方法的时间复杂度有所降低。
结论
2
提出一种基于密集网络和改进的监督核哈希方法，提高了图像特征的表达能力和投影精度，具有较好的检索性能和较低的时间复杂度；且所提方法的可拓展性也较好，不仅能够有效应用到彩色图像检索领域，也可以应用在医学灰度图像检索领域。
Objective
2
To extract image features that can fully express image semantic information
reduce projection errors in Hash retrieval
and generate more compact binary Hash codes
a method based on dense network and improved supervised Hashing with kernels is proposed.
Method
2
The pre-processed image data set is used to train the dense network. To reduce the over-fitting phenomenon
L2 regularization term is added into the cross entropy as a new loss function. When the dense network model is training
batch normalization (BN) algorithm and root mean square prop (RMSProp) optimization algorithm are used to improve the accuracy and robustness of the model. High-level semantic features of images with trained and optimized dense network model are removed to enhance the ability of image features to express image information and build an image feature library of the image dataset. The kernel principal component analysis projection is then performed on the extracted image features. The nonlinear information implicit in the image features is fully exploited to reduce the projection error. The supervised kernel Hash method is also used to supervise the image features
enhance the resolution of the linear inseparable image feature data
and map the features to the Hamming space. According to the correspondence between the inner product of Hash code and Hamming distance and the semantic similarity monitoring matrix composed of image label information
the Hamming distance is optimized to generate a more compact binary Hash code. Next
the image feature Hash code library of the image dataset is constructed. Finally
the same operation is performed on the input query image to obtain the Hash code of the query image. The Hamming distance between the Hash code of the query image and the Hash code of the image feature in the image dataset is compared to measure the similarity. The retrieved similar images are returned in ascending order.
Result
2
To verify the effectiveness
expansion
and efficiency of the proposed method
our method is used respectively in Paris6K and lung nodule analysis 16(LUNA16) datasets. It is also compared with other six commonly used Hashing methods. The average retrieval accuracy is compared in 12
24
32
48
64
and 128 bits of code length. Experimental results show that the average retrieval accuracy increases with the increase of Hash code length. When the Hash code length increases to a certain value
the average retrieval accuracy decreases. The average retrieval accuracy of the proposed method is always higher than that of the other six Hash methods. Except for the semantic Hashing method
the average retrieval accuracy value reaches the maximum when the Hash code length is 48 bits. Other Hash methods
including the proposed method
have the maximum average retrieval accuracy value when the Hash code length is 64 bits
and the retrieval accuracy is better. When the Hash code length is 64 bits
the average retrieval accuracy value of the proposed method is as high as 89.2% and 92.9% in the Paris6K and LUNA16 datasets
respectively. The time complexity of the proposed method and the convolutional neural network (CNN) Hashing method is compared in the Paris6K and LUNA16 data sets when the Hash code length is 12
24
32
48
64
and 128 bits. Results show that the time complexity of the proposed method is reduced under different Hash code lengths and is efficient to a certain degree.
Conclusion
2
A method based on dense network and improved supervised Hashing with kernels is proposed. This method improves the expression ability of image features and projection accuracy and is superior to other similar methods in average retrieval accuracy
recall rate
and precision rate. It improves the retrieval performance to some extent. It has a lower time complexity of algorithm than the method of CNN Hashing method. In addition
the proposed method has better extensibility
which can be used not only in the field of color image retrieval but also in the field of medical gray scale image retrieval.
Armato III S G, McLennan G, Bidaut L, McNitt-Gray M F, Meyer C R, Reeves A P, Zhao B S, Aberle D R, Henschke C I, Hoffman E A, Kazerooni E A, MacMahon H, van Beek E J. R, Yankelevitz D, Biancardi A M, Bland P H, Brown M S, Engelmann R M, Laderach G E, Max D, Pais R C, Qing D P Y, Roberts R Y, Smith A R, Starkey A, Batra P, Caligiuri P, Farooqi A, Gladish G W, Jude C M, Munden R F, Petkovska I, Quint L E, Schwartz L H, Sundaram B, Dodd L E, Fenimore C, Gur D, Petrick N, Freymann J, Kirby J, Hughes B, Vande C A, Gupte S, Sallam M, Heath M D, Kuhn M H, Dharaiya E, Burns R, Fryd D S, Salganicoff M, Anand V, Shreter U, Vastagh S, Croft B Y and Clarke L P. 2011. The lung image database consortium (LIDC) and image database resource initiative (IDRI):a completed reference database of lung nodules on CT scans. Medical Physics, 38(2):915-931[DOI:10.1118/1.3528204]
Dalal N and Triggs B. 2005. Histograms of oriented gradients for human detection//Proceedings of 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition. San Diego, CA, USA: IEEE Computer Society: 886-893[ DOI: 10.1109/CVPR.2005.177 http://dx.doi.org/10.1109/CVPR.2005.177 ]
Gong Y C, Lazebnik S, Gordo A and Perronnin F. 2013. Iterative quantization:a procrustean approach to learning binary codes for large-scale image retrieval. IEEE Transactions on Pattern Analysis and Machine Intelligence, 35(12):2916-2929[DOI:10.1109/TPAMI.2012.193]
Hotelling H. 1993. Analysis of a complex of statistical variables into principal components. Journal of Educational Psychology, 24(6):417-441[DOI:10.1037/h0071325]
Har-Peled S, Indyk P and Motwani R. 2012. Approximate nearest neighbor:towards removing the curse of dimensionality. Theory of Computing, 8(1):321-350[DOI:10.4086/toc.2012.v008a014]
He K M, Zhang X Y, Ren S Q and Sun J. 2016. Deep residual learning for image recognition//Proceedings of 2016 IEEE Conference on Computer Vision and Pattern Recognition. Las Ve gas, NV, USA: IEEE: 770-778[ DOI: 10.1109/CVPR.2016.90 http://dx.doi.org/10.1109/CVPR.2016.90 ]
Huang G, Liu Z, van der Maaten L and Weinberger K Q. 2016. Densely connected convolutional networks[EB/OL ] .[2019-05-31 ] . https://arxiv.org/pdf/1608.06993v5.pdf https://arxiv.org/pdf/1608.06993v5.pdf
Ioffe S and Szegedy C. 2015. Batch normalization: Accelerating deep network training by reducing internal covariate shift//Proceedings of the 32nd International Conference on Machine Learning. Lille, France: ACM: 448-456
Kang X D. 2009. Image Informatics. Beijing:People's Medical Publishing House:191
康晓东. 2009.影像信息学.北京:人民卫生出版社:191
Krizhevsky A, Sutskever I and Hinton G E. 2012. ImageNet classification with deep convolutional neural networks//Proceedings of the 25th International Conference on Neural Information Processing Systems. Lake Tahoe, Nevada: Curran Associates Inc: 1097-1105[ DOI: 10.1145/3065386 http://dx.doi.org/10.1145/3065386 ]
Kulis B and Darrell T. 2009. Learning to Hash with binary reconstructive embeddings //Proceedings of the 22nd International Conference on Neural Information Processing Systems. Vancouver, British Columbia, Canada: Curran Associates Inc: 1042-1050[ DOI: http://espace.library.uq.edu.aulview/UQ:192948 http://dx.doi.org/http://espace.library.uq.edu.aulview/UQ:192948 ]
Lai H J, Pan Y, Liu Y and Yan S C. 2015. Simultaneous feature learning and Hash coding with deep neural networks//Proceedings of 2015 IEEE Conference on Computer Vision and Pattern Recognition. Boston, MA, USA: IEEE: 3270-3278[ DOI: 10.1109/CVPR.2015.7298947 http://dx.doi.org/10.1109/CVPR.2015.7298947 ]
LeCun Y, Bengio Y and Hinton G. 2015. Deep learning. Nature, 521(7553):436-444[DOI:10.1038/nature14539]
Li W J and Zhou Z H. 2015. Learning to Hash for big data:current status and future trends. Chinese Science Bulletin, 60(5/6):485-490
李武军, 周志华. 2015.大数据哈希学习:现状与趋势.科学通报, 60(5/6):485-490[DOI:10.1360/N972014-00841]
Lin K, Yang H F, Hsiao J and Chen C S. 2015. Deep learning of binary Hash codes for fast image retrieval//Proceedings of 2015 IEEE Conference on Computer Vision and Pattern Recognition Workshops. Boston, MA, USA: IEEE: 27-35[ DOI: 10.1109/CVPRW.2015.7301269 http://dx.doi.org/10.1109/CVPRW.2015.7301269 ]
Liu W, Wang J, Ji R R, Jiang Y G and Chang S F. 2012. Supervised Hashing with kernels//Proceedings of 2012 IEEE Conference on Computer Vision and Pattern Recognition. Providence, RI, USA: IEEE Press: 2074-2081[ DOI: 10.1109/CVPR.2012.6247912 http://dx.doi.org/10.1109/CVPR.2012.6247912 ]
Lowe D G. 2004. Distinctive image features from scale-invariant keypoints. International Journal of Computer Vision, 60(2):91-110[DOI:10.1023/B:VISI.0000029664.99615.94]
Luan T T, Zhu J H, Xu S Y, Wang J X, Shi X and Li Y C. 2019. Hashing method for image retrieval based on product quantization with Huffman coding. Journal of Image and Graphics, 24(3):389-399
栾婷婷, 祝继华, 徐思雨, 王佳星, 时璇, 李垚辰. 2019.哈夫曼编码乘积量化的图像哈希检索方法.中国图象图形学报, 24(3):389-399[DOI:10.11834/jig.180264]
Philbin J, Chum O, Isard M, Sivic J and Zisserman A. 2008. Lost in quantization: improving particular object retrieval in large scale image databases//Proceedings of 2008 IEEE Conference on Computer Vision and Pattern Recognition. Anchorage, AK, USA: IEEE: 1-8[ DOI: 10.1109/CVPR.2008.4587635 http://dx.doi.org/10.1109/CVPR.2008.4587635 ]
Raginsky M and Lazebnik S. 2009. Locality-sensitive binary codes from shift-invariant kernels//Proceedings of the 22nd International Conference on Neural Information Processing Systems. Vancouver, British Columbia, Canada: ACM: 1509-1517
Razavian A, Azizpour H, Sullivan J and Carlsson S. 2014. CNN features off-the-shelf: an astounding baseline for recognition//Proceedings of 2014 IEEE Conference on Computer Vision and Pattern Recognition Workshops. Columbus, OH, USA: IEEE: 806-813[ DOI: 10.1109/CVPRW.2014.131 http://dx.doi.org/10.1109/CVPRW.2014.131 ]
Salakhutdinov R and Hinton G. 2009. Semantic Hashing. International Journal of Approximate Reasoning, 50(7):969-978[DOI:10.1016/j.ijar.2008.11.006]
Shen F M, Zhou X, Yang Y, Song J K, Shen H T and Tao D C. 2016. A fast optimization method for general binary code learning. IEEE Transactions on Image Processing, 25(12):5610-5621[DOI:10.1109/TIP.2016.2612883]
Simonyan K and Zisserman A. 2014. Very deep convolutional networks for large-scale image recognition[EB/OL ] .[2019-05-31 ] . https://arxiv.org/pdf/1409-1556.pdf https://arxiv.org/pdf/1409-1556.pdf
Tielemant and Hinton G. 2012. Rmsprop: Divide the Gradient by A Running Average of Its Recent Magnitude, 4(2), 26-31
Vadlamudi L N, Vaddella R P V and Devara V. 2017. Robust image Hashing technique for content authentication based on DWT//Raman B, Kumar S, Roy P P and Sen D, eds. Proceedings of International Conference on Computer Vision and Image Processing. Singapore: Springer: 181-191[ DOI: 10.1007/978-981-10-2104-6_17 http://dx.doi.org/10.1007/978-981-10-2104-6_17 ]
Wang J, Kumar S and Chang S F. 2010. Semi- supervised Hashing for scalable image retrieval//Proceedings of 2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition. San Francisco, CA, USA: IEEE Press: 3424-3431[ DOI: 10.1109/CVPR.2010.5539994 http://dx.doi.org/10.1109/CVPR.2010.5539994 ]
Weiss Y, Torralba A and Fergus R. 2008. Spectral Hashing//Proceedings of the 22nd Annual Conference on Neural Information Processing Systems. Vancouver: Curran Associates Inc: 1753-1760
Xia R K, Pan Y, Lai H J, Liu C and Yan S C. 2014. Supervised Hashing for image retrieval via image representation learning//Proceedings of the 28th AAAI Conference on Artificial Intelligence. Quebec City, Canada: AAAI: 2156-2162
Yang H F, Lin K and Chen C S. 2018. Supervised learning of semantics-preserving Hash via deep convolutional neural networks. IEEE Transactions on Pattern Analysis and Machine Intelligence, 40(2):437-451
相关作者
相关机构
京公网安备11010802024621
