作者投稿和查稿 主编审稿 专家审稿 编委审稿 远程编辑

计算机工程

• 图形图像处理 • 上一篇    下一篇

基于语义的图像低层可视特征提取及应用

韩冬梅1,2,王 雯1,李博斐1   

  1. (1. 上海财经大学信息管理与工程学院,上海 200433;2. 上海市金融信息技术研究重点实验室,上海 200433)
  • 收稿日期:2013-09-09 出版日期:2014-03-15 发布日期:2014-03-13
  • 作者简介:韩冬梅(1961-),女,教授、博士生导师,主研方向:图像特征提取,数据挖掘,语义网;王 雯,博士研究生;李博斐,硕士研究生。
  • 基金资助:
    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)。

Extraction and Application of Image Low-level Visual Features Based on Semantics

HAN Dong-mei 1,2, WANG Wen 1, LI Bo-fei 1   

  1. (1. School of Information Management and Engineering, Shanghai University of Finance and Economics, Shanghai 200433, China; 2. Shanghai Key Laboratory of Financial Information Technology, Shanghai 200433, China)
  • Received:2013-09-09 Online:2014-03-15 Published:2014-03-13

摘要: 为实现图像低层可视特征提取及其智能语义推理,从遥感图像解译入手,结合灰度共生矩阵和模糊C均值分类器提取图像纹理特征。构造基于灰度形态学的多尺度多结构元素边缘检测算子,提取特征知识。构建基于断层带的多源地学数据语义推理模型。以成都附近的断层为研究对象,进行语义推理验证,其解译结果与专家实地解译情况相符,初步验证该模型的可行性,使图像的机器分析结果更加贴近专业人员的目视解译,为地学研究数字化和遥感图像解译信息化提供参考。

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

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.

Key words: semantic Web, texture feature, edge feature, semantic reasoning, Gray Level Co-occurrence Matrix(GLCM), multi-source geosciences data

中图分类号: