Ps22Pdf 关键词 : 语义图像检索 ; 相关反馈 ; 关键字网络 [gap=813]Key words: Semantic Image Retrieval; Relevant Feedback; Keyword Networks
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基于高层语义图像检索 High-level Semantic-Based Image Retrieval
基于语义的图像检索 Semantic-based Image Retrieval ; SBIR
语义的图像检索 Semantic-based Image Retrieval
图像语义检索 Semantic-based Image Retrieval
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实验表明本文提出的图像颜色特征提取算法可成功应用于海量图像库检索和图像语义信息的自动提取。
Experiments show that the new algorithm proposed can be successfully used in retrieving the image from good-sized image database and extracting semantic information from image automatically.
针对图像检索中的语义鸿沟问题，提出了一种新颖的自动图像标注方法。
A novel automatic image annotation approach is proposed to bridge the semantic gap of content-based image retrieval.
因此，如何结合语义特征，使得所抽取的低层物理特征和图像内容所表示的语义特征之间建立良好的联系，实现高效的图像检索，仍是很长一段时间内需要研究解决的问题之一。
Therefore, it is still an unsolved problem about how to integrate with semantic features to achieve better connection between the lower physical features and the image content for efficient retrieval.
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