Interactive-Time Similarity Search for Large Image Collections Using Parallel VA-Files
Interactive-Time Similarity Search for Large Image Collections Using Parallel VA-Files
复制标题
使用并行 VA 文件对大型图像集合进行交互时相似性搜索
DOI：
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发表时间：
2000
期刊：
影响因子：
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通讯作者：
H. Schek
中科院分区：
文献类型：
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作者：
R. Weber;Klemens Böhm;H. Schek
在数字图书馆中，基于内容的多媒体对象检索中，最近邻搜索（NN-search）起着关键作用。然而，现有的神经网络搜索技术的性能对于大型集合和对象的高维表示并不令人满意。为了获得交互的响应时间，我们采用以下方法：它使用线性算法处理向量的近似值并将其并行化。更详细地说，我们基于网络工作站（NOW）中的va文件并行化nn搜索。这种方法将大型集合的搜索时间减少到合理的水平。我们观察到的最佳加速是在只有三个组件和900 MB特征数据的NOW中提高了近30倍。但这需要许多设计决策，特别是在考虑负载动态和组件的异质性时。我们的贡献就是解决这些设计问题。
In digital libraries, nearest-neighbor search (NN-search) plays a key role for content-based retrieval over multimedia objects. However, performance of existing NN-search techniques is not satisfactory with large collections and with high-dimensional representations of the objects. To obtain response times that are interactive, we pursue the following approach: it uses a linear algorithm that works with approximations of the vectors and parallelizes it. In more detail, we parallelize NN-search based on the VA-File in a Network of Workstations (NOW). This approach reduces search time to a reasonable level for large collections. The best speedup we have observed is by almost 30 for a NOW with only three components with 900 MB of feature data. But this requires a number of design decisions, in particular when taking load dynamism and heterogeneity of components into account. Our contribution is to address these design issues.