计算机工程Issue(3)：244-248,5.DOI:10.3969/j.issn.1000-3428.2014.03.051
基于语义的图像低层可视特征提取及应用
Extraction and Application of Image Low-level Visual Features Based on Semantics
摘要
Abstract
In order to realize extraction of image low-level visual features and semantic reasoning, this paper starts from remote sensing image explanations, combines Gray Level Co-occurrence Matrix(GLCM) and Fuzzy C-Means(FCM) classifier to extract texture feature, then detects edge by multi-scale and multi-structuring elements based on grayscale morphology, finally constructs multi-sources geological data based on the fault zone and uses the Chengdu parcels to test and verify the model. The results completely coincide with the expert’s field studies, which demonstrates the feasibility of this model, makes the results of machine analysis closer to results of visual interpretation, and provides valuable preferences fordigitalization of the earth science study and informationization of image interpretation.关键词
语义网/纹理特征/边缘特征/语义推理/灰度共生矩阵/多源地学数据Key words
semantic Web/texture feature/edge feature/semantic reasoning/Gray Level Co-occurrence Matrix(GLCM)/multi-source geosciences data分类
信息技术与安全科学引用本文复制引用
韩冬梅,王雯,李博斐..基于语义的图像低层可视特征提取及应用[J].计算机工程,2014,(3):244-248,5.基金项目
国家自然科学基金资助项目“基于语义网的多源地学空间数据融合与挖掘研究”(41174007)。 （41174007）
