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首页> 外文会议>4th International Conference on New Trends in Information Science and Service Science >Symbolic image indexing and retrieval by spatial similarity: A new approach based on multi-dimensional B+ tree
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Symbolic image indexing and retrieval by spatial similarity: A new approach based on multi-dimensional B+ tree

机译：基于空间相似度的符号图像索引与检索：一种基于多维B + 树的新方法

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In recent years, researchers have paid attention to image retrieval increasingly. By increasing the image volumes in databases, symbolic image that were used as summaries for physical images, seemed a good approach. This images are retrieved with shorter time than physical images and are cost-effective. In this research we introduce a new approach for symbolic image indexing and retrieving by using multi-dimensional B+-tree. Spiral relations multi-dimensional indexing mechanism is used for image retrieval. Different methods were proposed for retrieving this kind of images. Among those, the best method from speed and inflexibility point of view was making one dimensional B+-tree by using TSR algorithm that understands three-dimensional relations between image components. This would help database systems to be intelligent, flexible and speedy. We draw a B+ - tree for all images in database by using the low level colour feature and create multi-dimonsional B+-tree by combining it with the B+-tree that we made it for symbolic images by using triangle spatial relations(TSR). Empirical results showed that the presented method would have many advantages in comparison to one-dimensional B+-tree method.
机译：近年来，研究人员越来越重视图像检索。通过增加数据库中的图像量，用作物理图像摘要的符号图像似乎是一种不错的方法。该图像的检索时间比物理图像要短，并且具有成本效益。在这项研究中，我们介绍了一种使用多维B + -tree进行符号图像索引和检索的新方法。螺旋关系多维索引机制用于图像检索。提出了不同的方法来检索这种图像。其中，从速度和灵活性的角度来看，最好的方法是使用理解图像分量之间的三维关系的TSR算法制作一维B + -树。这将帮助数据库系统变得智能，灵活和快速。我们使用低级颜色功能为数据库中的所有图像绘制B + -树，并通过将其与B + -树> + -树，我们通过使用三角形空间关系（TSR）将其用于符号图像。实验结果表明，与一维B + -树方法相比，该方法具有很多优势。

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