Image Retrieval Based on Wavelet Scattering Network

Wen Jie-hua; Tan Li-hui

doi:10.12052/gdutxb.180037

English Version Journal Login

您当前的位置：

首页 >

文章列表页 >

Image Retrieval Based on Wavelet Scattering Network

更新时间：2026-05-27

- Image Retrieval Based on Wavelet Scattering Network
- Vol. 36, Issue 1, Pages: 63-67, 80(2019)
- 作者机构：
  
  广东工业大学　应用数学学院,广东,广州,510520
- 作者简介：
- 基金信息：
- DOI：10.12052/gdutxb.180037
  CLC： O235
- Online First：25 January 2019，
  
  Published：2019
- 稿件说明：
移动端阅览
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.

摘要

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.

Abstract

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.

关键词

Keywords

references

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.

Views

下载量

CSCD

Alert me when the article has been cited

提交

Tools

Publicity Resources

A Resource Allocation Optimization Method for Multi-UAV Integrated Sensing, Computing, and Communication Towards Disaster Relief

Robust Design for Artificial Noise-assisted UAV Terahertz Secure Communication

An Optimal Operation Strategy for Community Microgrid Alliances Considering Power Exchange under Multi-package Shared Energy Storage Model

The Cross-twin Physics-informed Neural Network for Wave Equations with Conserved Quantities

Multi-Scale Contrastive Embedding Enhanced Adaptive Intrusion Detection Model

Related Author

No data

Related Institution

No data

AI问答

⁰