Tang Biao, Jin Wei, Li Gang, et al. The cloud retrieval of combining sparse representation with subspace projection[J]. Opto-Electronic Engineering, 2019, 46(10): 180627. doi: 10.12086/oee.2019.180627
Citation: Tang Biao, Jin Wei, Li Gang, et al. The cloud retrieval of combining sparse representation with subspace projection[J]. Opto-Electronic Engineering, 2019, 46(10): 180627. doi: 10.12086/oee.2019.180627

The cloud retrieval of combining sparse representation with subspace projection

    Fund Project: Supported by National Natural Science Foundation of China (61471212) and the Natural Science Foundation of Zhejiang Province of China (LY16F010001)
More Information
  • Satellite cloud imagery can show the features and the evolution processes of all kinds of cloud systems from different aspects. Thus, adopting the content-based cloud image retrieval makes a big difference in supervising present weather conditions and studying the climate change. In order to optimize the combined features of the cloud picture and strengthen the generalization ability of its combined features, this paper presents an optimal method of combining the features of the sparse representation with the subspace projection. At first, we should extract its color, texture and shape, convert all the combined features, and divide them into different blocks. Then, we can make the sparse representation for each block's features, grouping them according to different atom variance and gaining both noticeable and unnoticeable features. Finally, we can count the power of the grouped features to get the subspace projection matrix, projecting the original combined features on it and achieving the optimal cloud picture features. The experiment turns out that the method of optimizing the cloud picture features in this paper is better than common descending dimension method and cloud retrieval technology in precision ratio and recall ratio. It indeed has a stronger optimization in the combined features as well as a lower time complexity in the process of the real-time retrieval, which indicates a brand new retrieval method.
  • 加载中
  • [1] Gurve M K, Sarup J. Satellite cloud image processing and information retrieval system[C]//2012 World Congress on Information and Communication Technologies, 2012: 292–296.

    Google Scholar

    [2] Ou S S C, Kahn B H, Liou K N, et al. Retrieval of cirrus cloud properties from the atmospheric infrared sounder: the k-coefficient approach using cloud-cleared radiances as input[J]. IEEE Transactions on Geoscience and Remote Sensing, 2013, 51(2): 1010–1024. doi: 10.1109/TGRS.2012.2205261

    CrossRef Google Scholar

    [3] 甘玲, 邹宽中, 刘肖.基于PCA降维的多特征级联的行人检测[J].计算机科学, 2016, 43(6): 308–311.

    Google Scholar

    Gan L, Zou K Z, Liu X. Pedestrian detection based on PCA dimension reduction of multi-feature cascade[J]. Computer Science, 2016, 43(6): 308–311.

    Google Scholar

    [4] 吴贤伟, 邰晓英, 巴特尔.基于内容的彩色胃镜图像检索[J].计算机应用, 2005, 25(S1): 248–250.

    Google Scholar

    Wu X W, Tai X Y, Ba T E. Content based color gastroscopy image retrieval[J]. Computer Applications, 2005, 25(S1): 248–250.

    Google Scholar

    [5] 华顺刚, 周羽, 刘婷.基于PCA+LDA的热红外成像人脸识别[J].模式识别与人工智能, 2008, 21(2): 160–164. doi: 10.3969/j.issn.1003-6059.2008.02.006

    CrossRef Google Scholar

    Hua S G, Zhou Y, Liu T. Thermal infrared face image recognition based on PCA and LDA[J]. Pattern Recognition and Artificial Intelligence, 2008, 21(2): 160–164. doi: 10.3969/j.issn.1003-6059.2008.02.006

    CrossRef Google Scholar

    [6] 王慧, 宋淑蕴.基于KCPA提取特征和RVM的图像分类[J].吉林大学学报(理学版), 2017, 55(2): 357–362. doi: 10.13413/j.cnki.jdxblxb.2017.02.28

    CrossRef Google Scholar

    Wang H, Song S Y. Image classification based on KCPA feature extraction and RVM[J]. Journal of Jilin University (Science Edition), 2017, 55(2): 357–362. doi: 10.13413/j.cnki.jdxblxb.2017.02.28

    CrossRef Google Scholar

    [7] 赵洪伟, 谢永芳, 曹斌芳, 等.基于Gabor小波和LPP的浮选过程泡沫纹理特征提取及应用[J].上海交通大学学报, 2014, 48(7): 942–947.

    Google Scholar

    Zhao H W, Xie Y F, Cao B F, et al. Extraction and application of froth texture feature based on Gabor wavelets and LPP in flotation process[J]. Journal of Shanghai Jiaotong University, 2014, 48(7): 942–947.

    Google Scholar

    [8] 王宝锋, 刘俊, 王国宇, 等.基于拉普拉斯特征映射法的水下图像降维研究[J].现代电子技术, 2013, 36(2): 29–31. doi: 10.3969/j.issn.1004-373X.2013.02.011

    CrossRef Google Scholar

    Wang B F, Liu J, Wang G H, et al. Research on underwater image dimensionality reduction based on Laplacian Eigenmap[J]. Modern Electronics Technique, 2013, 36(2): 29–31. doi: 10.3969/j.issn.1004-373X.2013.02.011

    CrossRef Google Scholar

    [9] 颜文.基于内容的云图检索技术研究[D].宁波: 宁波大学, 2017.

    Google Scholar

    Yan W. Study on content-based cloud image retrieval technology[D]. Ningbo: Ningbo University, 2017.

    Google Scholar

    [10] 李秀馨.基于内容的卫星云图检索技术研究[D].南京: 南京航空航天大学, 2013.

    Google Scholar

    Li X X. Research on the technology of content-based satellite cloud image retrieval[D]. Nanjing: Nanjing University of Aeronautics and Astronautics, 2013.

    Google Scholar

    [11] 上官伟.基于内容的卫星云图处理与信息检索技术研究[D].哈尔滨: 哈尔滨工程大学, 2008.

    Google Scholar

    Shangguan W. Research of content-based satellite cloud image progressing and information retrieval technology[D]. Harbin: Harbin Engineering University, 2008.

    Google Scholar

    [12] 刘英春.卫星云图在天气分析及预报中的应用[J].农业与技术, 2016, 36(22): 207.

    Google Scholar

    Liu Y C. Application of satellite cloud image in weather analysis and forecast[J]. Agriculture & Technology, 2016, 36(22): 207.

    Google Scholar

    [13] 宋小燕, 白福忠, 武建新, 等.应用灰度直方图特征识别木材表面节子缺陷[J].激光与光电子学进展, 2015, 52(3): 031501. doi: 10.3788/LOP52.031501

    CrossRef Google Scholar

    Song X Y, Bai F Z, Wu J X, et al. Wood knot defects recognition with gray-scale histogram features[J]. Laser & Optoelectronics Progress, 2015, 52(3): 031501. doi: 10.3788/LOP52.031501

    CrossRef Google Scholar

    [14] 吴煌鹏, 戴声奎.基于ULBP特征子空间的2DLDA人脸识别方法[J].模式识别与人工智能, 2014, 27(10): 894–899. doi: 10.3969/j.issn.1003-6059.2014.10.005

    CrossRef Google Scholar

    Wu H P, Dai S K. Face recognition of 2DLDA based on ULBP eigensubspace[J]. Pattern Recognition and Artificial Intelligence, 2014, 27(10): 894–899. doi: 10.3969/j.issn.1003-6059.2014.10.005

    CrossRef Google Scholar

    [15] 田文哲, 符冉迪, 金炜, 等.面向卫星云图云分类的自适应模糊支持向量机[J].武汉大学学报·信息科学版, 2017, 42(4): 488–495. doi: 10.13203/j.whugis20140734

    CrossRef Google Scholar

    Tian W Z, Fu R D, Jin W, et al. Adaptive fuzzy support vector machine for classification of clouds in satellite imagery[J]. Geomatics and Information Science of Wuhan University, 2017, 42(4): 488–495. doi: 10.13203/j.whugis20140734

    CrossRef Google Scholar

    [16] 高仕博, 程咏梅, 肖利平, 等.面向目标检测的稀疏表示方法研究进展[J].电子学报, 2015, 43(2): 320–332. doi: 10.3969/j.issn.0372-2112.2015.02.018

    CrossRef Google Scholar

    Gao S B, Cheng Y M, Xiao L P, et al. Recent advances of sparse representation for object detection[J]. Acta Electronica Sinica, 2015, 43(2): 320–332. doi: 10.3969/j.issn.0372-2112.2015.02.018

    CrossRef Google Scholar

    [17] 周颖, 符冉迪, 颜文, 等.基于结构组稀疏表示的红外云图超分辨率方法[J].光电工程, 2016, 43(12): 126–132. doi: 10.3969/j.issn.1003-501X.2016.12.020

    CrossRef Google Scholar

    Zhou Y, Fu R D, Yan W, et al. A method of infrared nephogram super-resolution based on structural group sparse representation[J]. Opto-Electronic Engineering, 2016, 43(12): 126–132. doi: 10.3969/j.issn.1003-501X.2016.12.020

    CrossRef Google Scholar

    [18] 夏士明, 李骞, 张璟, 等.一种基于灰度共生矩阵的云图检索方法[J].现代计算机, 2013(25): 34–38. doi: 10.3969/j.issn.1007-1423(z).2013.17.009

    CrossRef Google Scholar

    Xia S M, Li Q, Zhang J, et al. Satellite cloud image retrieval based on gray level co-occurrence matrix[J]. Modern Computer, 2013(25): 34–38. doi: 10.3969/j.issn.1007-1423(z).2013.17.009

    CrossRef Google Scholar

    [19] 李艳兵, 李元祥, 孙龙祥, 等.基于小波纹理特征的卫星云图检索[C]//第十二届全国图象图形学学术会议论文集, 2005: 5.

    Google Scholar

    Li Y B, Li Y X, Sun L X, et al. Satellite cloud image retrieval based on wavelet texture[C]//Proceedings of the 12th National Conference on Image and Graphics, 2005: 5.

    Google Scholar

    [20] Ji N, Zuo D, Cao Y, et al. Image classification with deep dictionary and sparse representation[J]. Wireless Communication Technology, 2017, 26(4): 56–60.

    Google Scholar

  • Overview: The satellite cloud image can show the characteristics of the cloud system and its evolution process from multiple angles. The research of cloud image retrieval is of great significance for weather monitoring and climate research. In the design and implementation of cloud image retrieval system, feature extraction is the key link. Since different types of cloud image features have their own advantages when portraying cloud images, combining different types of features will help improve the performance of cloud image retrieval system. However, since the combined cloud image features tend to be too high in dimension, the time cost is often high in the similarity measurement phase, and there is redundancy between the feature vectors. In view of the above problems, this paper combines sparse representation and subspace projection technology to propose a dimension reduction method for cloud image combination features. Firstly, the content information of the cloud image is drawn from different angles, that is, the three features of the cloud image color, texture and shape are extracted. After that, the three features are normalized and cascaded into a combined feature vector, and then the combined feature vector is matrixed. The form is arranged, and the matrix is subsequently subjected to block processing. Then, each block is sparsely represented, the variance of each feature block sparse representation coefficient is calculated, and the feature blocks are grouped according to the variance obtained by different blocks and grouped by grouping. It is then possible to separate the initial combined features into salient features and non-significant features. At this point, a subspace will be searched, in which the significant part is preserved, and the non-significant part will be suppressed. A projection matrix can be obtained by learning training to achieve the combined feature dimension reduction. In the retrieval stage, the initial cloud image combination feature vector is projected on the projection matrix, and the dimensionality reduction cloud image feature can be obtained, so that the cloud image retrieval can be realized quickly and accurately. The experimental results show that the cloud image retrieval system achieved by this method is superior to the traditional dimensionality reduction method in the accuracy and recall rate of the cloud image retrieval system, and the time complexity of the retrieval process is low. This indicates that the proposed method has strong dimensionality reduction ability for cloud image combination features, and provides a new idea for cloud image feature dimension reduction and efficient cloud image retrieval system.

  • 加载中
通讯作者: 陈斌, bchen63@163.com
  • 1. 

    沈阳化工大学材料科学与工程学院 沈阳 110142

  1. 本站搜索
  2. 百度学术搜索
  3. 万方数据库搜索
  4. CNKI搜索

Figures(10)

Tables(7)

Article Metrics

Article views(7202) PDF downloads(2523) Cited by(0)

Access History

Other Articles By Authors

Article Contents

Catalog

    /

    DownLoad:  Full-Size Img  PowerPoint