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All fields Title Creator / Publisher Keyword Year AND OR AND NOT All fields Title Creator / Publisher Keyword Year AND OR AND NOT All fields Title Creator / Publisher Keyword Year AND OR AND NOT All fields Title Creator / Publisher Keyword Year AND OR AND NOT All fields Title Creator / Publisher Keyword Year Sort by creator [A → Z]' creator [Z → A]' publishing year ↑ (asc)' publishing year ↓ (desc)' title [A → Z]' title [Z → A]' Simple Search Page: 1 2 3 4 5...11 Hits 1 – 20 of 210 1 A Visualizable Evidence-Driven Approach for Authorship Attribution Ding; Steven H. H.; Fung... In: https://dl.acm.org/citation.cfm?doid=2744298.2699910 (2015) BASE Show details 2 Ten Years of Rich Internet Applications: A Systematic Mapping Study, and Beyond Casteleyn, Sven; Garrigós Fernández, Irene; Mazón López, José-Norberto. - : Digital Library, 2015 BASE Show details 3 On the localness of software Zhaopeng Tu; Zhendong Su; Prem Devanbu In: http://macbeth.cs.ucdavis.edu/cache-model.pdf (2014) BASE Show details 4 Approximate Semantic Matching of Events for the Internet of Things Hasan, Souleiman; Curry, Edward. - : ACM, 2014 BASE Show details 5 Q.: Scale based region growing for scene text detection Junhua Mao; Houqiang Li; Wengang Zhou; Shuicheng Yan; Qi Tian In: http://www.stat.ucla.edu/%7Ejunhua.mao/papers/Scale_based_region_growing_ACM_MM13.pdf (2013) Abstract: Scene text is widely observed in our daily life and has many im-portant multimedia applications. Unlike document text, scene text usually exhibits large variations in font and language, and suffers from low resolution, occlusions and complex background. In this paper, we present a novel scale-based region growing algorithm for scene text detection. We first distinguish SIFT features in text regions from those in background by exploring the inter- and intra-statistics of SIFT features. Then scene text regions in images are identified by scale-based region growing, which explores the geo-metric context of SIFT keypoints in local regions. Our algorithm is very effective to detect multilingual text in various fonts, sizes, and with complex background. In addition, it offers insights on efficiently deploying local features in numerous applications, such as visual search. We evaluate our algorithm on three datasets and achieve the state-of-the-art performance. Keyword: Categories and Subject Descriptors I.4.8 [Computing Methodologies; Experimentation; I.4.9 [Computing Methodologies; Image Processing and Com- puter Vision—scene analysis; Image Processing and Computer Vision—Applications General Terms Algorithms; Neural Network; Performance. Keywords Scene Text; Region Growing; SIFT URL: http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.673.1886 http://www.stat.ucla.edu/%7Ejunhua.mao/papers/Scale_based_region_growing_ACM_MM13.pdf BASE Hide details 6 Gigatensor: scaling tensor analysis up by 100 times - algorithms and discoveries U Kang; Evangelos Papalexakis; Abhay Harpale... In: http://www.cs.cmu.edu/~christos/PUBLICATIONS/kdd12-gigatensor.pdf (2012) BASE Show details 7 Distributional Semantics with Eyes: Using Image Analysis to Improve Computational Representations of Word Meaning Elia Bruni; Marco Baroni In: http://clic.cimec.unitn.it/marco/publications/bruni-etal-acmmm-2012.pdf (2012) BASE Show details 8 Integrating document clustering and . . . SHENGHUO ZHU, ,et al. In: http://users.cis.fiu.edu/~taoli/pub/a14-wang.pdf (2011) BASE Show details 9 Folks in folksonomies: social link prediction from shared metadata Rossano Schifanella; Alain Barrat In: http://hal.archives-ouvertes.fr/docs/00/42/98/86/PDF/wsdm141-schifanella.pdf (2010) BASE Show details 10 Semantic lexicon adaptation for use in query interpretation Ana-maria Popescu; Patrick Pantel; Gilad Mishne In: http://www.ra.ethz.ch/cdstore/www2010/www/p1167.pdf (2010) BASE Show details 11 Classifying latent user attributes in Twitter Delip Rao; David Yarowsky; Abhishek Shreevats. - 2010 BASE Show details 12 Classifying latent user attributes in twitter Delip Rao; David Yarowsky; Abhishek Shreevats... In: https://csc-869-mlog.googlecode.com/files/p37-rao.pdf (2010) BASE Show details 13 Spatiotemporal mapping of Wikipedia concepts Adrian Popescu; Gregory Grefenstette In: http://comupedia.org/adrian/articles/jcdl75-popescu.pdf (2010) BASE Show details 14 An Information-extraction system for Urdu—a resource-poor language Smruthi Mukund; Rohini Srihari; Erik Peterson In: http://www.cedar.buffalo.edu/~rohini/Papers/ACM-TALIP.pdf (2010) BASE Show details 15 Transliteration for resource-scarce languages Manoj K. Chinnakotla; Om P. Damani; Avijit Satoskar In: http://www.cse.iitb.ac.in/~damani/papers/TALIP10/transliterationTALIP10.pdf (2010) BASE Show details 16 MorphoNet: Exploring the Use of Community Structure for Unsupervised Morpheme Analysis Delphine Bernhard In: http://clef.isti.cnr.it/2009/working_notes/morpho-papers/bernhard-paperCLEF2009.pdf (2009) BASE Show details 17 OpinionMiner: a novel machine learning system for web opinion mining and extraction Wei Jin; Hung Hay Ho; Rohini K. Srihari In: http://www.cedar.buffalo.edu/~rohini/Papers/KDD_Jin.pdf (2009) BASE Show details 18 Word sense disambiguation: a survey Roberto Navigli In: http://www.dsi.uniroma1.it/~navigli/pubs/ACM_Survey_2009_Navigli.pdf (2009) BASE Show details 19 Sentiment analysis of blogs by combining lexical knowledge with text classification Prem Melville; Wojciech Gryc; Wolfson Bldg... In: http://www.prem-melville.com/publications/pooling-multinomials-kdd09.pdf (2009) BASE Show details 20 A 2-poisson model for probabilistic coreference of named entities for improved text retrieval Seung-hoon Na; Hwee Tou Ng In: http://www.comp.nus.edu.sg/~nght/pubs/sigir09.pdf (2009) BASE Show details Page: 1 2 3 4 5...11

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