LIU Tingting, LIN Hui, ZHANG Liangpei, DAI Hanqing. SVM-relevance-feedback and Semantic-extraction-based RS Image Retrieval[J]. Geomatics and Information Science of Wuhan University, 2012, 37(4): 415-418.
Citation: LIU Tingting, LIN Hui, ZHANG Liangpei, DAI Hanqing. SVM-relevance-feedback and Semantic-extraction-based RS Image Retrieval[J]. Geomatics and Information Science of Wuhan University, 2012, 37(4): 415-418.

SVM-relevance-feedback and Semantic-extraction-based RS Image Retrieval

Funds: 国家海洋局极地专项“测绘遥感技术在极地环境考察与评估中的应用”资助项目(JDZX20110008);;武汉大学青年教师资助项目(3101004)
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  • Received Date: January 14, 2012
  • Published Date: April 04, 2012
  • The semantic gap between high-level human perception and low-level image features becomes the bottleneck in content-based remotely sensed image retrieval technology.To solve this problem,in this research,a human machine interaction(HMI) remotely sensed image retrieval system is built that combines semantic mining model and SVM-based relevance feedback method.The experiments indicate that this method can well narrow semantic gap and improve retrieval precision and recall.
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