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Jaume Gibert, Ernest Valveny and Horst Bunke. 2010. Graph of Words Embedding for Molecular Structure-Activity Relationship Analysis. 15th Iberoamerican Congress on Pattern Recognition.30–37. (LNCS.)
Abstract: Structure-Activity relationship analysis aims at discovering chemical activity of molecular compounds based on their structure. In this article we make use of a particular graph representation of molecules and propose a new graph embedding procedure to solve the problem of structure-activity relationship analysis. The embedding is essentially an arrangement of a molecule in the form of a vector by considering frequencies of appearing atoms and frequencies of covalent bonds between them. Results on two benchmark databases show the effectiveness of the proposed technique in terms of recognition accuracy while avoiding high operational costs in the transformation.
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Salim Jouili, Salvatore Tabbone and Ernest Valveny. 2009. Comparing Graph Similarity Measures for Graphical Recognition. 8th IAPR International Workshop on Graphics Recognition. Springer. (LNCS.)
Abstract: In this paper we evaluate four graph distance measures. The analysis is performed for document retrieval tasks. For this aim, different kind of documents are used including line drawings (symbols), ancient documents (ornamental letters), shapes and trademark-logos. The experimental results show that the performance of each graph distance measure depends on the kind of data and the graph representation technique.
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Salim Jouili, Salvatore Tabbone and Ernest Valveny. 2009. Evaluation of graph matching measures for documents retrieval. In proceedings of 8th IAPR International Workshop on Graphics Recognition.13–21.
Abstract: In this paper we evaluate four graph distance measures. The analysis is performed for document retrieval tasks. For this aim, different kind of documents are used which include line drawings (symbols), ancient documents (ornamental letters), shapes and trademark-logos. The experimental results show that the performance of each grahp distance measure depends on the kind of data and the graph representation technique.
Keywords: Graph Matching; Graph retrieval; structural representation; Performance Evaluation
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Alfons Juan-Ciscar and Gemma Sanchez. 2008. PRIS 2008. Pattern Recognition in Information Systems. Proceedings of the 8th international Workshop on Pattern Recognition in Information systems – PRIS 2008, in conjunction with ICEIS 2008.
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Dimosthenis Karatzas. 2008. Detecting Gradients in Text Images Using the Hough Transform. Proceedings of the 8th International Workshop on Document Analysis Systems,.245–252.
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Dimosthenis Karatzas, Marçal Rusiñol, Coen Antens and Miquel Ferrer. 2008. Segmentation Robust to the Vignette Effect for Machine Vision Systems. 19th International Conference on Pattern Recognition.
Abstract: The vignette effect (radial fall-off) is commonly encountered in images obtained through certain image acquisition setups and can seriously hinder automatic analysis processes. In this paper we present a fast and efficient method for dealing with vignetting in the context of object segmentation in an existing industrial inspection setup. The vignette effect is modelled here as a circular, non-linear gradient. The method estimates the gradient parameters and employs them to perform segmentation. Segmentation results on a variety of images indicate that the presented method is able to successfully tackle the vignette effect.
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Muhammad Muzzamil Luqman, Thierry Brouard, Jean-Yves Ramel and Josep Llados. 2010. Vers une approche foue of encapsulation de graphes: application a la reconnaissance de symboles. Colloque International Francophone sur l'Écrit et le Document.169–184.
Abstract: We present a new methodology for symbol recognition, by employing a structural approach for representing visual associations in symbols and a statistical classifier for recognition. A graphic symbol is vectorized, its topological and geometrical details are encoded by an attributed relational graph and a signature is computed for it. Data adapted fuzzy intervals have been introduced for addressing the sensitivity of structural representations to noise. The joint probability distribution of signatures is encoded by a Bayesian network, which serves as a mechanism for pruning irrelevant features and choosing a subset of interesting features from structural signatures of underlying symbol set, and is deployed in a supervised learning scenario for recognizing query symbols. Experimental results on pre-segmented 2D linear architectural and electronic symbols from GREC databases are presented.
Keywords: Fuzzy interval; Graph embedding; Bayesian network; Symbol recognition
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Muhammad Muzzamil Luqman, Thierry Brouard, Jean-Yves Ramel and Josep Llados. 2010. A Content Spotting System For Line Drawing Graphic Document Images. 20th International Conference on Pattern Recognition.3420–3423.
Abstract: We present a content spotting system for line drawing graphic document images. The proposed system is sufficiently domain independent and takes the keyword based information retrieval for graphic documents, one step forward, to Query By Example (QBE) and focused retrieval. During offline learning mode: we vectorize the documents in the repository, represent them by attributed relational graphs, extract regions of interest (ROIs) from them, convert each ROI to a fuzzy structural signature, cluster similar signatures to form ROI classes and build an index for the repository. During online querying mode: a Bayesian network classifier recognizes the ROIs in the query image and the corresponding documents are fetched by looking up in the repository index. Experimental results are presented for synthetic images of architectural and electronic documents.
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W. Liu and Josep Llados. 2006. Graphics Recognition. Ten Years Review and Future Perspectives. (LNCS.)
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Josep Llados, Dimosthenis Karatzas, Joan Mas and Gemma Sanchez. 2008. A Generic Architecture for the Conversion of Document Collections into Semantically Annotated Digital Archives.
Keywords: Median Graph, Graph Embedding, Graph Matching, Structural Pattern Recognition
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