...( SVM) ; 低层特征 ; 高层语义 ; 基于内容的图像检索 [gap=1002]Key words: Support Vector Machine( SVM) ; Low-level Feature; High-level Semantic; Content-based Image Retrieval ..
基于6个网页-相关网页
Low-Level Feature Extraction 底层特征提取
low level feature 低层次要素
image low level feature 图像低层次特征
low-level visual feature 底层视觉特征
The result shows that the retrieval based on semantic has a good performance, and it shortens the gap between the high-level semantic and the low-level feature.
实验表明，加入语义的图像检索速度显著提高，有效的缩减了高层语义和低层特征的鸿沟。
参考来源 - 基于语义的自然图像检索及相关反馈算法研究—《电脑知识与技术》—2008年第36期—龙源期刊网·2,447,543篇论文数据，部分数据来源于NoteExpress
Users can provide their query requirement in several different manners, the best retrieval result will be presented for the low-level feature and semantic network can cooperate properly.
在检索的过程中，用户可以通过多种不同途径给出查询条件，图像的低层特征与语义网络互为补充以达到最佳的检索效果。
The thesis presents a semantic vector algorithm, builds up the network of image semantic keywords, and realizes the composite retrieval of the image low-level feature and semantics characteristics.
提出了一种语义向量算法，构建了图像语义关键词网络，实现了图像底层视觉特征和语义的复合索引。
This demonstrates a common feature in APM systems whereby low-level and specific metrics can be traced multiple times to higher-level name Spaces to provide summary-level metrics.
这展示了APM系统中的一个常见的特性，凭此特性可以多次将低级的和特定指标跟踪到更高级的名称空间，提供汇总的指标。
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