Algorithms of high-level semantic-based image retrieval

被引：0
|
作者：
Wang, Chong-Jun [1 ]
Yang, Yu-Bin [1 ]
Chen, Shi-Fu [1 ]
机构：
[1] Lab. for Novel Software Technol., Nanjing Univ., Nanjing 210093, China
来源：
Ruan Jian Xue Bao/Journal of Software | 2004年 / 15卷 / 10期
关键词：
Algorithms - Computational complexity - Content based retrieval - Feature extraction - Mathematical models - Probability - Semantics - Statistical methods - Thesauri;
D O I：
暂无
中图分类号：
学科分类号：
摘要：
IPSM is an integrated probabilistic image semantic description multi-level model. This model includes input layer, feature layer, semantic layer, synthetical probability layer, probability propagation layer, and semantic mapping layer. Based on the model and characterizing of the image high-level semantic content according to Bayesian theory, SHM (semantic high-level retrieval algorithm) and SRF (high-level semantic relevance feedback algorithm) for image retrieval based on high-level semantic content, for user relevance feedback respectively, are designed and implemented. Experimental results indicate that IPSM, SHM and SRF are effective in characterizing image high-level semantic content and can provide sound and robust image retrieval performance.
引用
收藏
页码：1461 / 1469
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