Towards content-based retrieval of technical drawings through high-dimensional indexing
This paper presents a new approach to classify, index and retrieve technical drawings by content. Our work uses spatial relationships, visual elements and high-dimensional indexing mechanisms to retrieve complex drawings from CAD databases. This contrasts with conventional approaches which use mostly textual metadata for the same purpose. Creative designers and draftspeople often reuse data from previous projects, publications and libraries of ready to use components. Usually, retrieving these drawings is a slow, complex and error-prone endeavor, requiring either exhaustive visual examination, a solid memory, or both. Unfortunately, the widespread use of CAD systems, while making it easier to create and edit drawings, exacerbates this problem, insofar as the number of projects and drawings grows enormously, without providing adequate retrieval mechanisms to support retrieving these documents. In this paper, we describe an approach that supports automatic indexing of technical drawing databases through drawing simplification techniques based on geometric features and efficient algorithms to index large amounts of data. We describe in detail the indexing structure (NB-Tree) we have developed within the context of a more general approach. Experimental evaluation reveals that our approach outperforms some of the best indexing structures published, enabling us to search very large drawing databases.
content-based retrieval high-dimensional indexing graph matching