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Efficient Symbol Retrieval by Building a Symbol Index from a Collection of Line Drawings

机译:通过从线图集合中建立符号索引来进行有效的符号检索

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Symbol retrieval is important for content-based search in digital libraries and for automatic interpretation of line drawings. In this work, we present a complete symbol retrieval system. The proposed system has an off-line content-analysis stage, where the contents of a database of line drawings are represented as a symbol index, which is a compact indexable representation of the database. Such representation allows efficient on-line query retrieval. Within the retrieval system, three methods are presented. First, a feature grouping method for identifying local regions of interest (ROIs) in the drawings. The found ROIs represent symbols' parts. Second, a clustering method based on geometric matching, is used to cluster the similar parts from all the drawings together. A symbol index is then constructed from the clusters' representatives. Finally, the ROIs of a query symbol are matched to the clusters' representatives. The matching symbols' parts are retrieved from the clusters, and spatial verification is performed on the matching parts. By using the symbol index we are able to achieve a query look-up time that is independent of the database size, and dependent on the size of the symbol index. The retrieval system achieves higher recall and precision than state-of-the-art methods.
机译:符号检索对于数字图书馆中基于内容的搜索以及线图的自动解释非常重要。在这项工作中,我们提出了一个完整的符号检索系统。所提出的系统具有离线内容分析阶段,其中线图数据库的内容表示为符号索引,该符号索引是数据库的紧凑的可索引表示。这种表示允许有效的在线查询检索。在检索系统中,提出了三种方法。首先,一种用于在附图中识别局部关注区域(ROI)的特征分组方法。找到的ROI表示符号的部分。其次,使用基于几何匹配的聚类方法将来自所有图形的相似零件聚类在一起。然后从聚类的代表构造符号索引。最后,查询符号的投资回报率与集群的代表相匹配。从聚类中检索匹配符号的部分,并对匹配部分执行空间验证。通过使用符号索引,我们可以实现查询查询时间,该查询时间与数据库大小无关,并且取决于符号索引的大小。与最新方法相比,该检索系统可实现更高的查全率和准确性。

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