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Query processing issues in region-based image databases

机译：基于区域的图像数据库中的查询处理问题

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摘要

Many modern image database systems adopt a region-based paradigm, in which images are segmented into homogeneous regions in order to improve the retrieval accuracy. With respect to the case where images are dealt with as a whole, this leads to some peculiar query processing issues that have not been investigated so far in an integrated way. Thus, it is currently hard to understand how the different alternatives for implementing the region-based image retrieval model might impact on performance. In this paper, we analyze in detail such issues, in particular the type of matching between regions (either one-to-one or many-to-many). Then, we propose a novel ranking model, based on the concept of Skyline, as an alternative to the usual one based on aggregation functions and k-Nearest Neighbors queries. We also discuss how different query types can be efficiently supported. For all the considered scenarios we detail efficient index-based algorithms that are provably correct. Extensive experimental analysis shows, among other things, that: (1) the 1–1 matching type has to be preferred to the N–M one in terms of efficiency, whereas the two have comparable effectiveness, (2) indexing regions rather than images performs much better, and (3) the novel Skyline ranking model is consistently the most efficient one, even if this sometimes comes at the price of a reduced effectiveness.
机译：许多现代图像数据库系统采用基于区域的范式，其中将图像分割成均匀的区域，以提高检索精度。关于整体处理图像的情况，这导致了一些特殊的查询处理问题，这些问题到目前为止还没有以集成的方式进行研究。因此，当前很难理解用于实现基于区域的图像检索模型的不同替代方案如何影响性能。在本文中，我们详细分析了此类问题，尤其是区域之间的匹配类型（一对一或多对多）。然后，我们基于Skyline的概念，提出了一种新颖的排名模型，作为基于聚合函数和k-最近邻居查询的常用模型的替代。我们还将讨论如何有效地支持不同的查询类型。对于所有考虑的场景，我们都会详细说明有效的基于索引的算法，这些算法被证明是正确的。广泛的实验分析表明，除其他事项外，（1）就效率而言，1-1匹配类型必须比N–M更为可取，而两者具有相当的有效性，（2）索引区域而不是图像表现要好得多，并且（3）新颖的Skyline排名模型始终是最有效的模型，即使有时这样做会降低有效性。

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