Nearest neighbor search for relevance feedback
Nearest neighbor search for relevance feedback
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最近邻搜索相关反馈
DOI：
10.1109/cvpr.2003.1211527
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发表时间：
2003
期刊：
影响因子：
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通讯作者：
B. S. Manjunath
中科院分区：
文献类型：
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作者：
Jelena Tešić;B. S. Manjunath
我们在相关反馈中引入了重复最近邻搜索的问题，并提出了一种高效的高维特征空间搜索方案。相关反馈学习是基于内容的图像和视频检索中支持高层次概念查询的一种常用方法。该文件解决了这些情况下，在相关反馈搜索的每次迭代过程中更新的相似性或距离矩阵和一组新的最近的邻居计算。这种在高维特征空间中的重复最近邻计算是昂贵的，特别是当数据集中的项目数量很大时。在这种情况下，我们提出了一种搜索算法，支持相关反馈的一般二次距离度量。该方案利用两个连续的最近邻集之间的相关性，从而显着降低了整体搜索的复杂性。使用60维纹理特征数据集提供了详细的实验结果。
We introduce the problem of repetitive nearest neighbor search in relevance feedback and propose an efficient search scheme for high dimensional feature spaces. Relevance feedback learning is a popular scheme used in content based image and video retrieval to support high-level concept queries. The paper addresses those scenarios in which a similarity or distance matrix is updated during each iteration of the relevance feedback search and a new set of nearest neighbors is computed. This repetitive nearest neighbor computation in high dimensional feature spaces is expensive, particularly when the number of items in the data set is large. In this context, we suggest a search algorithm that supports relevance feedback for the general quadratic distance metric. The scheme exploits correlations between two consecutive nearest neighbor sets thus significantly reducing the overall search complexity. Detailed experimental results are provided using 60 dimensional texture feature dataset.