| 摘要: |
| 利用Bayes统计学习和决策理论,建立了一种图像语义综合概率描述模型(image probability semanticmodel,简称IPSM).该模型是一种基于描述性特征建模方法的分层体系结构,由原始图像层、图像特征层、图像语义层、综合概率层、概率传播层和语义映射层6个部分组成.并在IPSM模型对图像的语义分类特征进行描述和提取的基础上,提出并实现了基于高层语义的图像检索算法(semantic high-1evel retrieval algorithm,简称SHM)以及基于高层语义的相关反馈算法(semantic relevance feedback,简称SRF).实验结果表明,IPSM模型及SHR和SRF两个算法能够有效地对图像的高层语义进行刻画,其图像匹配检索效果良好,并具有稳定的检索性能. |
| 关键词: SHM 语义特征提取 Bayes统计学习 语义分类辞典 相关反馈 |
| DOI： |
| 分类号: |
| 基金项目:Supported by the Natural Science Foundation of Jiangsu Province of China under Grant No.BJ200009(江苏省自然科学基金) |
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| Algorithms of High-Level Semantic-Based Image Retrieval |
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WANG Chong-Jun,YANG Yu-Bin,CHEN Shi-Fu
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| Abstract: |
| 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) 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. |
| Key words: SHM semantic feature extraction Bayesian statistical learning semantic thesaurus relevance feedback |
