广东工业大学 应用数学学院,广东,广州,510520
文介华(1990−),男,硕士研究生,主要研究方向为深度学习和图像识别及处理. E-mail:277844284@qq.com
网络首发:2019-01-25,
纸质出版:2019
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文介华, 谭立辉. 基于小波散射网络下的图像检索[J]. 广东工业大学学报, 2019, 36(1): 63-67, 80.
Wen Jie-hua, Tan Li-hui. Image Retrieval Based on Wavelet Scattering Network[J]. 2019, 36(1): 63-67, 80.
文介华, 谭立辉. 基于小波散射网络下的图像检索[J]. 广东工业大学学报, 2019, 36(1): 63-67, 80. DOI: 10.12052/gdutxb.180037.
Wen Jie-hua, Tan Li-hui. Image Retrieval Based on Wavelet Scattering Network[J]. 2019, 36(1): 63-67, 80. DOI: 10.12052/gdutxb.180037.
小波散射卷积神经网络由于其优越的性能而被迅速、广泛地运用到图像、音频等领域. 本文利用这种新型的网络结构提取图像的特征
结合相似度度量方法
实现该特征提取在图像检索方面的应用. 此外
将小波散射网络得到的特征系数
以其均值和方差作为新的特征
实现大规模图像检索的降维. 最后
利用实验算法对比验证了上述降维方法的可行性和优越性.
Due to superior performance of the wavelet scattering network
it is rapidly and widely used in the fields of image and audio. This new type of network structure is used to extract the features of the image and combines similarity measurement methods to achieve the application of this feature extraction in image retrieval. In addition
the eigenvalues obtained by the wavelet scattering network are taken as its new features
taking the mean value and the variance as the new features
so as to realize the dimensionality reduction of large-scale image retrieval. Finally
the experimental algorithm is used to verify the feasibility and superiority of the above dimensionality reduction method.
AKGUI C B, RUBIN D L, NAPEL S. Content-based image retrieval in radiology: current status and future directions [J]. Journal of Digital Imaging, 2011, 24(2): 208-222.
KHODASKAR A, LADHAKE S. A novel approach for content based image retrieval in context of combination S C techniques[C]//LADHAKE S. International Conference on Computer Communication and Informatics. Coimbatore: IEEE, 2015: 1-6.
ZHANG K J, DU J J. Texture image retrieval based on wavelet directional wave transform and gray level co-occurrence matrix [J]. Journal of Xuzhou Institute of Technology(Natural Sciences Edition), 2016, 31(4): 65-69.
YE Z J, WANG F L. An improved FREAK algorithm for image feature point matching [J]. Journal of Guangdong University of Technology, 2017, 34(6): 37-42.
ZAGORUYKO S, KOMODAKIS N. Learning to compare image patches via convolutional neural networks[C]// Computer Vision and Pattern Recognition. Boston: IEEE , 2015: 4353-4361.
HE K, ZHANG X, REN S. Spatial pyramid pooling in deep convolutional networks for visual recognition [J]. IEEE Transactions on Pattern Analysis Machine Intelligence, 2015, 37(9): 1904-1916.
LECUN Y, BOTTOU L. Gradient-based learning applied to document recognition [J]. Proceedings of the IEEE, 1998, 86(11): 2278-2324.
MALLAT S. Group invariant scattering [J]. Communications on Pure Applied Mathematics, 2012, 65(10): 1331-1398.
JOAKIM A, VINCENT L, STEPHANE M. Joint time-frequency scattering for Audio classification[C]// IEEE International Workshop on Machine Learning for Signal Processing (MLSP). Boston:IEEE, 2015: 1-6.
BRUNA J, MALLAT S, BACRY E. Intermittent process analysis with scattering moments [J]. Annals of Statistics, 2015, 43(1): 1-1.
SIFRE L, MALLAT S. Rotation, scaling and deformation invariant scattering for texture discrimination[C]// Computer Vision and Pattern Recognition. Portland: IEEE , 2013: 1233-1240.
WU H J, ZHANG M X, ZHEN J L. Texture segmentation based on wavelet scattering convolution network [J]. Microelectronics Computer, 2013, 30(5): 31-34.
WU J S, JIANG L Y, HAN X. Performance evaluation of wavelet scattering network in image texture classification in various color spaces [J]. Journal of Southeast University, 2015, 31(1): 46-50.
WIATOWSKI T, BÖLCSKEI H. A mathematical theory of deep convolutional neural networks for feature extraction [J]. IEEE Transactions on Information Theory, 2015, 64(3): 1845-1866.
JIN H J, ZENG T. Application of wavelet transform to image retrieval in HSV color space [J]. Electronic Measurement Technology, 2016, 39(7): 106-109.
SINGH S M, HEMACHANDRAN K. Content-based image retrieval using color moment and gabor texture feature [J]. International Journal of Computer Science Issues, 2012, 9(5): 719-724.
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