当前位置: X-MOL 学术Earth Sci. Inform. › 论文详情
Our official English website, www.x-mol.net, welcomes your feedback! (Note: you will need to create a separate account there.)
Automatic annotation of satellite images with multi class support vector machine
Earth Science Informatics ( IF 2.8 ) Pub Date : 2020-05-23 , DOI: 10.1007/s12145-020-00471-8
Joshua Bapu J , Jemi Florinabel D

Automatic Image Annotation (AIA) is used in image retrieval systems to retrieve the images by predicting tags for images. To achieve image retrieval with high accuracy, an automatic image annotation approach by using Multiclass SVM with the hybrid kernel is proposed. The hybrid kernel is a combination of Radial Basis Function (RBF) and Polynomial Kernel which overcomes the drawbacks of single kernels such as less accuracy, high computational complexity, etc. This technique exploits the Linear Binary Pattern- Discrete Wavelet Transform (LBP-DWT) feature extraction technique to extract the features in horizontal, vertical, and diagonal directions. The experiments suggest that the multiclass SVM can attain a higher accuracy than other conventional SVM with any single kernels. The Multiclass SVM can achieve high accuracy as 95.61% and increases the accuracy by 3.26%, 1.79%, and Kappa coefficient by 3.22%, 2.27% when compared with SVM RBF kernel, polynomial kernel respectively.

中文翻译:

使用多类支持向量机自动注释卫星图像

自动图像注释(AIA)用于图像检索系统中,通过预测图像标签来检索图像。为了实现高精度的图像检索,提出了一种将Multiclass SVM与混合内核结合使用的自动图像标注方法。混合内核是径向基函数(RBF)和多项式内核的组合,它克服了单个内核的缺点,如精度较低,计算复杂度较高等。此技术利用了线性二进制模式离散小波变换(LBP-DWT)特征提取技术,用于提取水平,垂直和对角线方向上的特征。实验表明,与任何具有单个内核的其他传统SVM相比,多类SVM可以获得更高的精度。Multiclass SVM可以达到95的高精度。
更新日期:2020-05-23
down
wechat
bug