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Author |
X. Jing; David Zhang; Zhong Jin |
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Title  |
Improvements on the uncorrelated optimal discriminant vectors |
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2003 |
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Pattern Recognition, 36(8): 1921–1923 (IF: 1.611) |
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no |
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Admin @ si @ JZJ2003a |
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428 |
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Author |
Hugo Jair Escalante; Jose Martinez; Sergio Escalera; Victor Ponce; Xavier Baro |
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Title |
Improving Bag of Visual Words Representations with Genetic Programming |
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2015 |
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IEEE International Joint Conference on Neural Networks IJCNN2015 |
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The bag of visual words is a well established representation in diverse computer vision problems. Taking inspiration from the fields of text mining and retrieval, this representation has proved to be very effective in a large number of domains.
In most cases, a standard term-frequency weighting scheme is considered for representing images and videos in computer vision. This is somewhat surprising, as there are many alternative ways of generating bag of words representations within the text processing community. This paper explores the use of alternative weighting schemes for landmark tasks in computer vision: image
categorization and gesture recognition. We study the suitability of using well-known supervised and unsupervised weighting schemes for such tasks. More importantly, we devise a genetic program that learns new ways of representing images and videos under the bag of visual words representation. The proposed method learns to combine term-weighting primitives trying to maximize the classification performance. Experimental results are reported in standard image and video data sets showing the effectiveness of the proposed evolutionary algorithm. |
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Killarney; Ireland; July 2015 |
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IJCNN |
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HuPBA;MV |
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Admin @ si @ EME2015 |
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2603 |
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Author |
Fadi Dornaika; Angel Sappa |
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Title |
Improving Appearance-Based 3D Face Tracking Using Sparse Stereo Data |
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Miscellaneous |
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2006 |
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International Conference on Computer Vision Theory and Applications, (2): 310–317 |
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ADAS @ adas @ DoS2006a |
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636 |
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Fadi Dornaika; Angel Sappa |
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Title |
Improving Appearance-Based 3D Face Tracking Using Sparse Stereo Data |
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Conference Article |
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2007 |
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Advances in Computer Graphics and Computer Vision, |
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354–366 |
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Springer Verlag |
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J. Braz, A. Ranchordas, H. Araujo and J. Jorge, |
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VISAPP |
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ADAS |
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ADAS @ adas @ DoS2007d |
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1046 |
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Author |
Ivan Huerta; Dani Rowe; Mikhail Mozerov; Jordi Gonzalez |
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Title |
Improving Background Subtraction based on a Casuistry of Colour-Motion Segmentation Problems |
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2007 |
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3rd Iberian Conference on Pattern Recognition and Image Analysis (IbPRIA 2007), J. Marti et al. (Eds.) LNCS 4478:475–482 |
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Girona (Spain) |
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ISE |
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ISE @ ise @ HRM2007 |
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783 |
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Author |
Arjan Gijsenij; Theo Gevers; Joost Van de Weijer |
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Title |
Improving Color Constancy by Photometric Edge Weighting |
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Journal Article |
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2012 |
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IEEE Transaction on Pattern Analysis and Machine Intelligence |
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TPAMI |
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34 |
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5 |
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918-929 |
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: Edge-based color constancy methods make use of image derivatives to estimate the illuminant. However, different edge types exist in real-world images such as material, shadow and highlight edges. These different edge types may have a distinctive influence on the performance of the illuminant estimation. Therefore, in this paper, an extensive analysis is provided of different edge types on the performance of edge-based color constancy methods. First, an edge-based taxonomy is presented classifying edge types based on their photometric properties (e.g. material, shadow-geometry and highlights). Then, a performance evaluation of edge-based color constancy is provided using these different edge types. From this performance evaluation it is derived that specular and shadow edge types are more valuable than material edges for the estimation of the illuminant. To this end, the (iterative) weighted Grey-Edge algorithm is proposed in which these edge types are more emphasized for the estimation of the illuminant. Images that are recorded under controlled circumstances demonstrate that the proposed iterative weighted Grey-Edge algorithm based on highlights reduces the median angular error with approximately $25\%$. In an uncontrolled environment, improvements in angular error up to $11\%$ are obtained with respect to regular edge-based color constancy. |
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Los Alamitos; CA; USA; |
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0162-8828 |
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CIC;ISE |
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Admin @ si @ GGW2012 |
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1850 |
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Author |
J. Chazalon; Marçal Rusiñol; Jean-Marc Ogier |
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Title |
Improving Document Matching Performance by Local Descriptor Filtering |
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Conference Article |
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2015 |
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6th IAPR International Workshop on Camera Based Document Analysis and Recognition CBDAR2015 |
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1216 - 1220 |
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In this paper we propose an effective method aimed at reducing the amount of local descriptors to be indexed in a document matching framework. In an off-line training stage, the matching between the model document and incoming images is computed retaining the local descriptors from the model that steadily produce good matches. We have evaluated this approach by using the ICDAR2015 SmartDOC dataset containing near 25 000 images from documents to be captured by a mobile device. We have tested the performance of this filtering step by using
ORB and SIFT local detectors and descriptors. The results show an important gain both in quality of the final matching as well as in time and space requirements. |
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Nancy; France; August 2015 |
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CBDAR |
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DAG; 600.077; 601.223; 600.084 |
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Admin @ si @ CRO2015a |
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2680 |
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Author |
Xavier Soria; Angel Sappa |
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Title |
Improving Edge Detection in RGB Images by Adding NIR Channel |
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Conference Article |
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2018 |
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14th IEEE International Conference on Signal Image Technology & Internet Based System |
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Edge detection; Contour detection; VGG; CNN; RGB-NIR; Near infrared images |
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The edge detection is yet a critical problem in many computer vision and image processing tasks. The manuscript presents an Holistically-Nested Edge Detection based approach to study the inclusion of Near-Infrared in the Visible spectrum
images. To do so, a Single Sensor based dataset has been acquired in the range of 400nm to 1100nm wavelength spectral band. Prominent results have been obtained even when the ground truth (annotated edge-map) is based in the visible wavelength spectrum. |
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Las Palmas de Gran Canaria; November 2018 |
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SITIS |
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MSIAU; 600.122 |
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Admin @ si @ SoS2018 |
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3192 |
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Author |
Hana Jarraya; Muhammad Muzzamil Luqman; Jean-Yves Ramel |
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Title |
Improving Fuzzy Multilevel Graph Embedding Technique by Employing Topological Node Features: An Application to Graphics Recognition |
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2017 |
Publication |
Graphics Recognition. Current Trends and Challenges |
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9657 |
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Springer |
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B. Lamiroy; R Dueire Lins |
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GREC |
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DAG; 600.097; 600.121 |
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Admin @ si @ JLR2017 |
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2928 |
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Muhammad Muzzamil Luqman; Jean-Yves Ramel; Josep Llados |
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Title |
Improving Fuzzy Multilevel Graph Embedding through Feature Selection Technique |
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Conference Article |
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2012 |
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Structural, Syntactic, and Statistical Pattern Recognition, Joint IAPR International Workshop |
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7626 |
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243-253 |
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Graphs are the most powerful, expressive and convenient data structures but there is a lack of efficient computational tools and algorithms for processing them. The embedding of graphs into numeric vector spaces permits them to access the state-of-the-art computational efficient statistical models and tools. In this paper we take forward our work on explicit graph embedding and present an improvement to our earlier proposed method, named “fuzzy multilevel graph embedding – FMGE”, through feature selection technique. FMGE achieves the embedding of attributed graphs into low dimensional vector spaces by performing a multilevel analysis of graphs and extracting a set of global, structural and elementary level features. Feature selection permits FMGE to select the subset of most discriminating features and to discard the confusing ones for underlying graph dataset. Experimental results for graph classification experimentation on IAM letter, GREC and fingerprint graph databases, show improvement in the performance of FMGE. |
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Springer Berlin Heidelberg |
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0302-9743 |
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978-3-642-34165-6 |
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SSPR&SPR |
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DAG |
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Admin @ si @ LRL2012 |
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2381 |
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Pau Torras; Arnau Baro; Alicia Fornes; Lei Kang |
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Title |
Improving Handwritten Music Recognition through Language Model Integration |
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Conference Article |
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2022 |
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4th International Workshop on Reading Music Systems (WoRMS2022) |
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42-46 |
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optical music recognition; historical sources; diversity; music theory; digital humanities |
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Handwritten Music Recognition, especially in the historical domain, is an inherently challenging endeavour; paper degradation artefacts and the ambiguous nature of handwriting make recognising such scores an error-prone process, even for the current state-of-the-art Sequence to Sequence models. In this work we propose a way of reducing the production of statistically implausible output sequences by fusing a Language Model into a recognition Sequence to Sequence model. The idea is leveraging visually-conditioned and context-conditioned output distributions in order to automatically find and correct any mistakes that would otherwise break context significantly. We have found this approach to improve recognition results to 25.15 SER (%) from a previous best of 31.79 SER (%) in the literature. |
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November 18, 2022 |
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WoRMS |
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DAG; 600.121; 600.162; 602.230 |
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Admin @ si @ TBF2022 |
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3735 |
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Author |
Andreas Fischer; Volkmar Frinken; Horst Bunke; Ching Y. Suen |
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Title |
Improving HMM-Based Keyword Spotting with Character Language Models |
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Conference Article |
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2013 |
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12th International Conference on Document Analysis and Recognition |
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506-510 |
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Facing high error rates and slow recognition speed for full text transcription of unconstrained handwriting images, keyword spotting is a promising alternative to locate specific search terms within scanned document images. We have previously proposed a learning-based method for keyword spotting using character hidden Markov models that showed a high performance when compared with traditional template image matching. In the lexicon-free approach pursued, only the text appearance was taken into account for recognition. In this paper, we integrate character n-gram language models into the spotting system in order to provide an additional language context. On the modern IAM database as well as the historical George Washington database, we demonstrate that character language models significantly improve the spotting performance. |
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Washington; USA; August 2013 |
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1520-5363 |
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ICDAR |
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DAG; 600.045; 605.203 |
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no |
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Admin @ si @ FFB2013 |
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2295 |
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Author |
Yainuvis Socarras; David Vazquez; Antonio Lopez; David Geronimo; Theo Gevers |
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Title |
Improving HOG with Image Segmentation: Application to Human Detection |
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Conference Article |
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2012 |
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11th International Conference on Advanced Concepts for Intelligent Vision Systems |
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7517 |
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178-189 |
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Segmentation; Pedestrian Detection |
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In this paper we improve the histogram of oriented gradients (HOG), a core descriptor of state-of-the-art object detection, by the use of higher-level information coming from image segmentation. The idea is to re-weight the descriptor while computing it without increasing its size. The benefits of the proposal are two-fold: (i) to improve the performance of the detector by enriching the descriptor information and (ii) take advantage of the information of image segmentation, which in fact is likely to be used in other stages of the detection system such as candidate generation or refinement.
We test our technique in the INRIA person dataset, which was originally developed to test HOG, embedding it in a human detection system. The well-known segmentation method, mean-shift (from smaller to larger super-pixels), and different methods to re-weight the original descriptor (constant, region-luminance, color or texture-dependent) has been evaluated. We achieve performance improvements of 4:47% in detection rate through the use of differences of color between contour pixel neighborhoods as re-weighting function. |
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Brno, Czech Republic |
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Springer Berlin Heidelberg |
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J. Blanc-Talon et al. |
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English |
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0302-9743 |
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978-3-642-33139-8 |
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ACIVS |
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ADAS;ISE |
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no |
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ADAS @ adas @ SLV2012 |
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1980 |
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Author |
Pau Riba; Anjan Dutta; Josep Llados; Alicia Fornes; Sounak Dey |
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Title |
Improving Information Retrieval in Multiwriter Scenario by Exploiting the Similarity Graph of Document Terms |
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Conference Article |
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2017 |
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14th International Conference on Document Analysis and Recognition |
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475-480 |
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document terms; information retrieval; affinity graph; graph of document terms; multiwriter; graph diffusion |
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Abstract |
Information Retrieval (IR) is the activity of obtaining information resources relevant to a questioned information. It usually retrieves a set of objects ranked according to the relevancy to the needed fact. In document analysis, information retrieval receives a lot of attention in terms of symbol and word spotting. However, through decades the community mostly focused either on printed or on single writer scenario, where the
state-of-the-art results have achieved reasonable performance on the available datasets. Nevertheless, the existing algorithms do not perform accordingly on multiwriter scenario. A graph representing relations between a set of objects is a structure where each node delineates an individual element and the similarity between them is represented as a weight on the connecting edge. In this paper, we explore different analytics of graphs constructed from words or graphical symbols, such as diffusion, shortest path, etc. to improve the performance of information retrieval methods in multiwriter scenario |
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DAG; 600.097; 601.302; 600.121 |
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Admin @ si @ RDL2017a |
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3053 |
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M. Bressan; Jordi Vitria |
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Improving Naive Bayes using Class Condicitonal ICA. |
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2002 |
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Iberoamerican Conference on Artificial Intelligence IBERAMIA 2002. |
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Sevilla, Espanya |
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BCNPCL @ bcnpcl @ BrV2002e |
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