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Author |
Volkmar Frinken; Markus Baumgartner; Andreas Fischer; Horst Bunke |
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Title |
Semi-Supervised Learning for Cursive Handwriting Recognition using Keyword Spotting |
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Conference Article |
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Year |
2012 |
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13th International Conference on Frontiers in Handwriting Recognition |
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49-54 |
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State-of-the-art handwriting recognition systems are learning-based systems that require large sets of training data. The creation of training data, and consequently the creation of a well-performing recognition system, requires therefore a substantial amount of human work. This can be reduced with semi-supervised learning, which uses unlabeled text lines for training as well. Current approaches estimate the correct transcription of the unlabeled data via handwriting recognition which is not only extremely demanding as far as computational costs are concerned but also requires a good model of the target language. In this paper, we propose a different approach that makes use of keyword spotting, which is significantly faster and does not need any language model. In a set of experiments we demonstrate its superiority over existing approaches. |
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Bari, Italy |
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10.1109/ICFHR.2012.268 |
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978-1-4673-2262-1 |
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ICFHR |
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DAG |
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no |
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Admin @ si @ FBF2012 |
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2055 |
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Author |
Emanuel Indermühle; Volkmar Frinken; Horst Bunke |
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Title |
Mode Detection in Online Handwritten Documents using BLSTM Neural Networks |
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Conference Article |
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Year |
2012 |
Publication |
13th International Conference on Frontiers in Handwriting Recognition |
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302-307 |
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Mode detection in online handwritten documents refers to the process of distinguishing different types of contents, such as text, formulas, diagrams, or tables, one from another. In this paper a new approach to mode detection is proposed that uses bidirectional long-short term memory (BLSTM) neural networks. The BLSTM neural network is a novel type of recursive neural network that has been successfully applied in speech and handwriting recognition. In this paper we show that it has the potential to significantly outperform traditional methods for mode detection, which are usually based on stroke classification. As a further advantage over previous approaches, the proposed system is trainable and does not rely on user-defined heuristics. Moreover, it can be easily adapted to new or additional types of modes by just providing the system with new training data. |
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Bari, italy |
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978-1-4673-2262-1 |
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ICFHR |
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DAG |
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no |
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Admin @ si @ IFB2012 |
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2056 |
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Author |
Ignasi Rius; Dani Rowe; Jordi Gonzalez; Xavier Roca |
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Title |
3D Action Modeling and Reconstruction for 2D Human Body Tracking |
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Miscellaneous |
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2005 |
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3rd International Conference on Advances in Pattern Recognition (ICAPR’2005), Pattern Recognition and Image Analysis, LNCS 3687: 146–154, ISBN 978–3–540–28833–6 |
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Bath (United Kingdom) |
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ISE |
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ISE @ ise @ RRG2005c |
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578 |
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Dani Rowe; Ignasi Rius; Jordi Gonzalez; Juan J. Villanueva |
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Title |
Improving Tracking by Handling Occlusions |
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Miscellaneous |
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2005 |
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3rd International Conference on Advances in Pattern Recognition (ICAPR’2005), Pattern Recognition and Image Analysis, LNCS 3687: 146–154, ISBN 978–3–540–28833–6 |
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Bath (United Kingdom) |
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no |
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ISE @ ise @ RRG2005d |
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619 |
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Author |
Quan-sen Sun; Zhong Jin; Pheng-ann Heng; De-shen Xia |
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Title |
A novel feature fusion method based on partial least squares regression |
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2005 |
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Pattern Recognition and Data Mining, Lecture Notes in Computer Science, 3686: 268–277 |
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Bath (United Kingdom) |
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no |
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Admin @ si @ SJH2005 |
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626 |
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Author |
Lluis Pere de las Heras; Joan Mas; Gemma Sanchez; Ernest Valveny |
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Title |
Wall Patch-Based Segmentation in Architectural Floorplans |
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Conference Article |
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Year |
2011 |
Publication |
11th International Conference on Document Analysis and Recognition |
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1270-1274 |
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Segmentation of architectural floor plans is a challenging task, mainly because of the large variability in the notation between different plans. In general, traditional techniques, usually based on analyzing and grouping structural primitives obtained by vectorization, are only able to handle a reduced range of similar notations. In this paper we propose an alternative patch-based segmentation approach working at pixel level, without need of vectorization. The image is divided into a set of patches and a set of features is extracted for every patch. Then, each patch is assigned to a visual word of a previously learned vocabulary and given a probability of belonging to each class of objects. Finally, a post-process assigns the final label for every pixel. This approach has been applied to the detection of walls on two datasets of architectural floor plans with different notations, achieving high accuracy rates. |
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Beiging, China |
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1520-5363 |
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978-0-7695-4520-2 |
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ICDAR |
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DAG |
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no |
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Admin @ si @ HMS2011a |
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1792 |
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Author |
Fadi Dornaika; Angel Sappa |
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Title |
Appearance-based 3D Face Tracker: An Evaluation Study |
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Miscellaneous |
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2005 |
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2nd IEEE Int. Workshop on Visual Surveillance and Performance Evaluation of Tracking and Surveillance, 121–128 |
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Beijing (China) |
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ADAS |
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no |
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ADAS @ adas @ DoS2005b |
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580 |
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Author |
Fadi Dornaika; Franck Davoine |
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Title |
Simultaneous Facial Action Tracking and Expression Recognition using a Particle Filter |
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Miscellaneous |
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2005 |
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10th IEEE Int. Conference on Computer Vision (ICCV) |
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Beijing (China) |
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no |
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Admin @ si @ DoD2005d |
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581 |
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Author |
Zhong Jin; Jing-Yu Yang; Zhen Lou |
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Title |
A luminance-conditional distribution model of skin color information |
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Miscellaneous |
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2005 |
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2005 Beijing International Conference on Imaging: Technology and Applications for the 21th Century, 280–281 |
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Beijing (China) |
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no |
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Admin @ si @ JYL2005 |
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628 |
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Author |
Sergio Escalera; Petia Radeva; Jordi Vitria; Xavier Baro; Bogdan Raducanu |
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Title |
Modelling and Analyzing Multimodal Dyadic Interactions Using Social Networks |
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Conference Article |
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2010 |
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12th International Conference on Multimodal Interfaces and 7th Workshop on Machine Learning for Multimodal Interaction. |
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Social interaction; Multimodal fusion, Influence model; Social network analysis |
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Social network analysis became a common technique used to model and quantify the properties of social interactions. In this paper, we propose an integrated framework to explore the characteristics of a social network extracted from
multimodal dyadic interactions. First, speech detection is performed through an audio/visual fusion scheme based on stacked sequential learning. In the audio domain, speech is detected through clusterization of audio features. Clusters
are modelled by means of an One-state Hidden Markov Model containing a diagonal covariance Gaussian Mixture Model. In the visual domain, speech detection is performed through differential-based feature extraction from the segmented
mouth region, and a dynamic programming matching procedure. Second, in order to model the dyadic interactions, we employed the Influence Model whose states
encode the previous integrated audio/visual data. Third, the social network is extracted based on the estimated influences. For our study, we used a set of videos belonging to New York Times’ Blogging Heads opinion blog. The results
are reported both in terms of accuracy of the audio/visual data fusion and centrality measures used to characterize the social network. |
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Beijing (China) |
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ICMI-MLI |
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OR;MILAB;HUPBA;MV |
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no |
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BCNPCL @ bcnpcl @ ERV2010 |
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1427 |
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Author |
Aura Hernandez-Sabate; David Rotger; Debora Gil |
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Title |
Image-based ECG sampling of IVUS sequences |
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Conference Article |
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2008 |
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Proc. IEEE Ultrasonics Symp. IUS 2008 |
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1330-1333 |
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Longitudinal Motion; Image-based ECG-gating; Fourier analysis |
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Longitudinal motion artifacts in IntraVascular UltraSound (IVUS) sequences hinders a properly 3D reconstruction and vessel measurements. Most of current techniques base on the ECG signal to obtain a gated pullback without the longitudinal artifact by using a specific hardware or the ECG signal itself. The potential of IVUS images processing for phase retrieval still remains little explored. In this paper, we present a fast forward image-based algorithm to approach ECG sampling. Inspired on the fact that maximum and minimum lumen areas are related to end-systole and end-diastole, our cardiac phase retrieval is based on the analysis of tissue density of mass along the sequence. The comparison between automatic and manual phase retrieval (0.07 ± 0.07 mm. of error) encourages a deep validation contrasting with ECG signals. |
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Beijing (China) |
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IAM;MILAB |
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no |
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IAM @ iam @ HRG2008 |
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1553 |
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Author |
Jose Manuel Alvarez; Antonio Lopez |
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Title |
Novel Index for Objective Evaluation of Road Detection Algorithms |
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Conference Article |
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2008 |
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Intelligent Transportation Systems. 11th International IEEE Conference on, |
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815–820 |
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road detection |
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Beijing (Xina) |
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ITSC |
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ADAS |
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ADAS @ adas @ AlL2008 |
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1074 |
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Marçal Rusiñol; David Aldavert; Ricardo Toledo; Josep Llados |
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Browsing Heterogeneous Document Collections by a Segmentation-Free Word Spotting Method |
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2011 |
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11th International Conference on Document Analysis and Recognition |
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63-67 |
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In this paper, we present a segmentation-free word spotting method that is able to deal with heterogeneous document image collections. We propose a patch-based framework where patches are represented by a bag-of-visual-words model powered by SIFT descriptors. A later refinement of the feature vectors is performed by applying the latent semantic indexing technique. The proposed method performs well on both handwritten and typewritten historical document images. We have also tested our method on documents written in non-Latin scripts. |
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Beijing, China |
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DAG;ADAS |
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no |
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Admin @ si @ RAT2011 |
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1788 |
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Volkmar Frinken; Andreas Fischer; Horst Bunke; Alicia Fornes |
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Title |
Co-training for Handwritten Word Recognition |
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Conference Article |
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2011 |
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11th International Conference on Document Analysis and Recognition |
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314-318 |
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To cope with the tremendous variations of writing styles encountered between different individuals, unconstrained automatic handwriting recognition systems need to be trained on large sets of labeled data. Traditionally, the training data has to be labeled manually, which is a laborious and costly process. Semi-supervised learning techniques offer methods to utilize unlabeled data, which can be obtained cheaply in large amounts in order, to reduce the need for labeled data. In this paper, we propose the use of Co-Training for improving the recognition accuracy of two weakly trained handwriting recognition systems. The first one is based on Recurrent Neural Networks while the second one is based on Hidden Markov Models. On the IAM off-line handwriting database we demonstrate a significant increase of the recognition accuracy can be achieved with Co-Training for single word recognition. |
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Beijing, China |
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DAG |
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no |
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Admin @ si @ FFB2011 |
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1789 |
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Author |
Muhammad Muzzamil Luqman; Jean-Yves Ramel; Josep Llados; Thierry Brouard |
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Title |
Subgraph Spotting Through Explicit Graph Embedding: An Application to Content Spotting in Graphic Document Images |
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Conference Article |
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2011 |
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11th International Conference on Document Analysis and Recognition |
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870-874 |
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Abstract |
We present a method for spotting a subgraph in a graph repository. Subgraph spotting is a very interesting research problem for various application domains where the use of a relational data structure is mandatory. Our proposed method accomplishes subgraph spotting through graph embedding. We achieve automatic indexation of a graph repository during off-line learning phase, where we (i) break the graphs into 2-node sub graphs (a.k.a. cliques of order 2), which are primitive building-blocks of a graph, (ii) embed the 2-node sub graphs into feature vectors by employing our recently proposed explicit graph embedding technique, (iii) cluster the feature vectors in classes by employing a classic agglomerative clustering technique, (iv) build an index for the graph repository and (v) learn a Bayesian network classifier. The subgraph spotting is achieved during the on-line querying phase, where we (i) break the query graph into 2-node sub graphs, (ii) embed them into feature vectors, (iii) employ the Bayesian network classifier for classifying the query 2-node sub graphs and (iv) retrieve the respective graphs by looking-up in the index of the graph repository. The graphs containing all query 2-node sub graphs form the set of result graphs for the query. Finally, we employ the adjacency matrix of each result graph along with a score function, for spotting the query graph in it. The proposed subgraph spotting method is equally applicable to a wide range of domains, offering ease of query by example (QBE) and granularity of focused retrieval. Experimental results are presented for graphs generated from two repositories of electronic and architectural document images. |
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Beijing, China |
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1520-5363 |
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978-1-4577-1350-7 |
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ICDAR |
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DAG |
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Admin @ si @ LRL2011 |
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1790 |
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