PPT-Image Annotation with Relevance Feedback

Inspirations ideas amp plans Motivation Ideal situation generalpurpose image annotation with unlimited vocabulary Reality Classifiers with limited vocabulary and dependency on labeled training data. for 2015 Baldrige Award Applicants . “Straight A” Feedback Comments. A. ctionable. . A. ccurate . A. dequate. A. ligned*. *. Added Emphasis in 2015. Actionable:. . relevant to the applicant’s key factors and specific enough that the organization can use the feedback to sustain or improve its . Tao Huang, . Shrideep. . Pallickara. , Geoffrey Fox. Community Grids Lab. Indiana University, Bloomington. . {. taohuang. , . spallick. , . gcf}@indiana.edu. Outline. Analysis of existing Collaboration and Annotation Systems. PropBank. . Outline. Introduction to the project. Basic linguistic concepts. Verb & Argument. Making information explicit. Null arguments. Tasks to be carried out. Timesheets, tips. Creation of Resources. By . Rong. Yan, Alexander G. and . Rong. Jin. Mwangi. S. . Kariuki. 2008-11629. Quiz. What’s Negative Pseudo-Relevance feedback in multimedia retrieval?. Introduction. As a result of high demand of content based access to video information.. 2011-11709. Seo. . Seok. . Jun. Abstract. Video information retrieval. Finding info. relevant to query. Approach. Pseudo-relevance feedback. Negative PRF. Questions. How this paper approach to content-based video retrieval. Massimo Poesio. University of . Essex. Part 1: Intro, . Microtask. . crowdsourcing. ANNOTATED CORPORA: . AN ERA OF PLENTY?. With the release of the . OntoNotes. and ANC corpora for English and a number of corpora for other languages (Prague Dependency Treebank especially) we may think we won’t need to do any more annotation for a while . A Close Reading Strategy for Better Comprehension. Text Annotation by Teachers. What is Text Annotation?. While reading, students mark the pages for . Important information. Text meaning or key details. Abstract. A large number of organizations today generate and share textual descriptions of their products, services, and actions. Such collections of textual data contain significant amount of structured information, which remains buried in the unstructured text. While information extraction algorithms facilitate the extraction of structured relations, they are often expensive and inaccurate, especially when operating on top of text that does not contain any instances of the targeted structured information. . Philip . McParlane. , Yashar Moshfeghi and Joemon M. Jose. University of Glasgow, UK. http://www.dcs.gla.ac.uk/~philip/. p.mcparlane.1@research.gla.ac.uk. Motivation for annotating images. Problems with existing automatic image annotation collections. 2. n-gram language model. 3. classifier. Studying Humour Features - Bolla, Whelan. Guidelines. define tags and describe how they should be applied. Data. 500 reviews of varying length (>7300 sentences). RuSSIR Young Scientist Conference,. 24-28| 2015| St. Petersburg, Russia. Arshad Khan. School of Electronics & Computer Science,. University of Southampton, UK. Overview. Searching in online repositories of multidisciplinary research data is becoming a challenge due to the volume and types of data being published every year. Authors: Joe Futrelle, Amber York. . @ Woods Hole Oceanographic Institution. Imaging FlowCytobot. (Heidi Sosik et al). HabCam. (Scott . Gallager. et al). SeaBED. (. Hanumat. Singh et al). Phytoplankton . ConceptRank. Petra Budíková, Michal Batko, Pavel Zezula. Outline. Search-based annotation. Motivation. Problem formalization. Challenges. ConceptRank. Idea. Semantic network construction. PageRank and ConceptRank. (ideas for . joint journal paper). CEMI meeting, Praha, 14. 3. 2014. Outline. Introduction. Why annotations?. State-of-the-art in multimedia annotation. Search-based image annotation. What. . we. .