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Content-based object organization for efficient image retrieval in image databases

机译:基于内容的对象组织,可在图像数据库中高效地检索图像

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摘要

Much research has focused on content-based image retrieval (CBIR) methods that can be automated in image classification and query processing. In this paper, we propose a blob-centric image retrieval scheme based on the blobworld representation. The blob-centric scheme consists of several newly proposed components, including an image classification method, an image browsing method based on semantic hierarchy of representative blobs, and a blob search method based on multidimensional indexing. We present the database structures and their maintenance algorithms for these components and conduct a performance comparison of three image retrieval methods, the naive method, the representative-blobs method, and the indexed-blobs method. Our quantitative analysis shows significant reduction in query response time by using the representative-blobs method and the indexed-blobs method.
机译:许多研究都集中在基于内容的图像检索(CBIR)方法上,该方法可以在图像分类和查询处理中实现自动化。在本文中,我们提出了一种基于Blobworld表示的以Blob为中心的图像检索方案。以斑点为中心的方案由几个新提出的组件组成,包括图像分类方法,基于代表性斑点的语义层次的图像浏览方法以及基于多维索引的斑点搜索方法。我们介绍了这些组件的数据库结构及其维护算法,并对三种图像检索方法(朴素方法,代表斑点方法和索引斑点方法)的性能进行了比较。我们的定量分析表明,通过使用代表blobs方法和索引blobs方法可以显着减少查询响应时间。

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