PPT-Semantic search-based image annotation

Petra Bud íková FI MU CEMI meeting Plze ň 1 6 4 2014 Formalization The annotation problem is defined by a query image I and a vocabulary V of candidate concepts. Scene Reprojection. Lei Yang. 1. , Yu-Chiu Tse. 1. , . Pedro Sander. 1. , Jason Lawrence. 2. , Diego Nehab. 3,4. , . Hugues. Hoppe. 3. , . Clara Wilkins. 5. 1. 2. 3. 4. 5. Outline. Reprojection and data reuse. Graphs. with . Metagraph. -based Learning. Yuan Fang. 1. , . Wenqing. Lin. 1. , Vincent Zheng. 2. ,. Min Wu. 1. , Kevin Chang. 23. , Xiao-Li Li. 1. . ICDE 2016 @ Helsinki. 1. Institute for . Infocomm. Andrew Chi. Brian Cristante. COMP 790-133: January 27, 2015. Image Retrieval. AI / Vision Problem. Systems Design / Software Engineering Problem. Sensory Gap. : “What features should we use?”. Query-Dependent?. Tutorial. Introduction. Miriam Fernandez | KMI, Open University, UK. Thanh Tran | Institute AIFB, KIT, DE. Peter Mika| Yahoo Research, Spain. Search . Document Retrieval vs. Data Retrieval. Differences of search technologies. 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. Nikhil . Rasiwasia. , . Nuno. . Vasconcelos. Statistical Visual Computing Laboratory. University of California, San Diego. Thesis Defense. Ill pause for a few moments so that you all can finish reading this. . 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. Opportunities. April 2015. Search – . Where we were!. https://pbs.twimg.com/media/B1sh79LIEAAR4Hg.jpg:large. https://encrypted-tbn0.gstatic.com/images?q=tbn:ANd9GcQ5sTW2_hPnqpRVmpHW2L4FFJ9SPuDaNF8TCTvH7xqUNXeo8Cqj-A. PRESENTED BY . Peter Mika, Sr. Research Scientist, Yahoo Labs. . ⎪ . November 27, . 2014 . The Semantic Web (2001-). 11/27/14. 2. Part of Tim . Berners-Lee’s . original proposal . for the . Web. (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. . Inspirations, ideas . &. plans. Motivation. Ideal situation: general-purpose image annotation with unlimited vocabulary. Reality:. Classifiers with limited vocabulary and dependency on labeled training data. Sequential Question Answering. Mohit Iyyer, Wen-tau Yih, Ming-Wei Chang. ACL-2017. Challenging research problem. Advocated in semantic parsing. [Pasupat & Liang 2015]. But, a . natural. way to interact with a question answering system?. C. Binding, K. May. 1. , R. Souza, D. Tudhope, A. Vlachidis. Hypermedia Research Unit, University of Glamorgan. 1. English Heritage. STAR Project - Aims. In. vestigate semantic technologies for integrating and cross searching datasets and associated grey literature.