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Josep Llados. 2006. Perspectives on the Analysis of Graphical Documents.
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Joan Mas, B. Lamiroy, Gemma Sanchez and Josep Llados. 2006. Automatic Adjacency Grammar Generation from User Drawn Sketches.
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Joan Mas, B. Lamiroy, Gemma Sanchez and Josep Llados. 2006. Automatic Learning of Symbol Descriptions Avoiding Topological Ambiguities.
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Joan Mas, Gemma Sanchez and Josep Llados. 2006. An Incremental Parser to Recognize Diagram Symbols and Gestures represented by Adjacency Grammars.
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Oriol Ramos Terrades. 2006. Linear Combination of Multiresolution Descriptors: Application to Graphics Recognition. (Ph.D. thesis, .)
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Jose Antonio Rodriguez, Gemma Sanchez and Josep Llados. 2006. Automatic Interpretation of Proofreading Sketches.
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Oriol Ramos Terrades, Salvatore Tabbone and Ernest Valveny. 2006. Combination of shape descriptors using an adaptation of boosting.
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Marçal Rusiñol and Josep Llados. 2006. Symbol Spotting in Technical Drawings Using Vectorial Signatures. Graphics Recognition: Ten Years Review and Future Perspectives, W. Liu, J. Llados (Eds.), LNCS 3926: 35–46.
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Salim Jouili, Salvatore Tabbone and Ernest Valveny. 2010. Comparing Graph Similarity Measures for Graphical Recognition. Graphics Recognition. Achievements, Challenges, and Evolution. 8th International Workshop, GREC 2009. Selected Papers. Springer Berlin Heidelberg, 37–48. (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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Albert Gordo, Florent Perronnin, Yunchao Gong and Svetlana Lazebnik. 2014. Asymmetric Distances for Binary Embeddings. TPAMI, 36(1), 33–47.
Abstract: In large-scale query-by-example retrieval, embedding image signatures in a binary space offers two benefits: data compression and search efficiency. While most embedding algorithms binarize both query and database signatures, it has been noted that this is not strictly a requirement. Indeed, asymmetric schemes which binarize the database signatures but not the query still enjoy the same two benefits but may provide superior accuracy. In this work, we propose two general asymmetric distances which are applicable to a wide variety of embedding techniques including Locality Sensitive Hashing (LSH), Locality Sensitive Binary Codes (LSBC), Spectral Hashing (SH), PCA Embedding (PCAE), PCA Embedding with random rotations (PCAE-RR), and PCA Embedding with iterative quantization (PCAE-ITQ). We experiment on four public benchmarks containing up to 1M images and show that the proposed asymmetric distances consistently lead to large improvements over the symmetric Hamming distance for all binary embedding techniques.
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