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Geometric-based Symbol Spotting and Retrieval in Technical Line Drawings

  • The automatic analysis and retrieval of technical line drawings is hindered by many challenges such as: the large amount of contextual clutter around the symbols within the drawings, degradation, transformations on the symbols in drawings, large databases of drawings and large alphabets of symbols. The core tasks required for the analysis of technical line drawings are: symbol recognition, spotting and retrieval. The current systems for performing these tasks have poor performance due to the mentioned challenges. This dissertation presents a number of methods that address these challenges. These methods achieve both accurate and efficient symbol spotting and retrieval in technical line drawings, and perform significantly better than state-of-the-art methods on the same problems. An overview of the key contributions of this dissertation is given in the following. First, this dissertation presents a geometric matching-based method for symbol recognition and spotting. The method performs recognition in the presence of large amounts of contextual clutter, and provides precise localization of the recognized symbols. On standard databases such as GREC-2005 and GREC-2011, the method achieves up to 10% higher recall and up to 28% higher precision than state-of-the-art methods on the spotting task, and achieves up to 7% higher recognition accuracy on the isolated recognition task. The method is based on a geometric matching approach, which is flexible enough to incorporate improvements on the matching strategy, feature types and information on the features. The method also includes an adaptive preprocessing algorithm that deals with a wide variety of noise types. In order to improve the performance of the spotting method when dealing with degraded drawings, two novel methods are presented in this dissertation. Both methods are based on combining geometric matching with machine learning techniques. The geometric matching is used to automatically generate training data that contain information on how well the features of the queries are matched in both the true and the false matches found by the spotting method. The first method learns the feature weights of the different query symbols by linear discriminant analysis (LDA). The weighted query features are used in the spotting method and result in 27% higher average precision than the original method, with a speedup factor of 2. The second method uses SVM classification as a post-spotting step to distinguish the true from the false matches in the spotting method. The use of the classification step further improves the average precision of the spotting method by 20.6%. This dissertation also presents methods for content analysis of line drawings. First, a method for accurate and consistent detection (95.8%) of regions of interest (ROIs) is presented. The method is based on statistical feature grouping. The ROI-finding method is identified as an important part of a symbol retrieval system: the better the detected ROIs,the higher the performance of a retrieval system. The ROI-finding method is also used to improve the performance of the geometric-based spotting system. Second, a symbol clustering method for building a compact and accurate representation of a large database of technical drawings is presented. This method uses the output from the ROI-finding method as input, and uses geometric matching as a similarity measure. The method achieves high accuracy (90.1% recall, 94.3% precision) in forming clusters of symbols. The representatives of the clusters (34 symbols) are used as key entries to a symbol index, which is identified as the outcome of an off-line stage of a symbol retrieval system. Finally, an efficient and high performing large scale symbol retrieval system is presented in this dissertation. The system follows the bag of visual words (BoVW) model, but with using methods that are suitable to line drawings. The system uses the symbol index to represent a database of drawings. During the on-line query retrieval stage, the query is analyzed by the ROI-finding method, matched with the key entries of the symbol index via geometric matching, and finally, a spatial verification step is performed on the retrieved matches. The system achieves a query lookup time that is independent of the size of the database, and is instead dependent on the size of the symbol index. The system achieves up to 10% higher recall and up to 28% higher precision than state-of-the-art spotting systems on similar databases. Overall, these contributions are major advancements in the research of graphics recognition. The hope is that, such contributions provide the basis for the development of reliable and accurate performing applications for browsing, querying or classification of line drawings for the benefit of end users.

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Metadaten
Author:Nibal Nayef
URN (permanent link):urn:nbn:de:hbz:386-kluedo-33865
Advisor:Thomas M. Breuel
Document Type:Doctoral Thesis
Language of publication:English
Publication Date:2013/01/16
Year of Publication:2013
Publishing Institute:Technische Universität Kaiserslautern
Granting Institute:Technische Universität Kaiserslautern
Acceptance Date of the Thesis:2012/12/14
Date of the Publication (Server):2013/01/17
Number of page:XV, 146
Faculties / Organisational entities:Fachbereich Informatik
DDC-Cassification:0 Allgemeines, Informatik, Informationswissenschaft / 004 Informatik
Licence (German):Standard gemäß KLUEDO-Leitlinien vom 10.09.2012