PPT-Relevance Feedback for the

Earth Movers Distance Marc Wichterich Christian Beecks Martin Sundermeyer Thomas Seidl Data Management and Data Exploration Group RWTH Aachen University Germany Introduction Distancebased Adaptable Similarity Search. Zhao School of Law University of Washington fczuwashingtonedu Douglas W Oard Coll of Info Stu UMIACS University of Maryland College Park MD 20742 oardumdedu Jason R Baron Of64257ce of the General Counsel National Archives and Records Administration 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 . Multimedia Databases. via Relevance Feedback . with History and Foresight Support. DBRank. 08, April 12. th. 2008, . Cancún. , Mexico. Marc Wichterich. , Christian Beecks, Thomas Seidl. Outline. Motivation. 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. Relevance Feedback . Relevance Feedback: . Example. Initial Results. Search Engine. 2. Relevance Feedback: . Example. Relevance Feedback. Search Engine. 3. Relevance Feedback: . Example. Revised Results. . Schütze. and Christina . Lioma. Lecture 9: Relevance Feedback & Query Expansion. 1. 2. Take-. away. . today. Interactive relevance feedback:. improve initial retrieval results by telling the IR system which docs are relevant / . Earth Mover‘s Distance. Marc Wichterich. , Christian Beecks, Martin Sundermeyer, Thomas Seidl. Data Management and Data Exploration Group. RWTH Aachen University, Germany. Introduction. Distance-based Adaptable Similarity Search. Sampath Jayarathna. Center for the Study of Digital Libraries. Computer Science & Engineering. Texas A&M University . Motivation. 2. Personalized Information Delivery. Customize search results based on the . Relevance Feedback: . Example. Initial Results. Search Engine. 2. Relevance Feedback: . Example. Relevance Feedback. Search Engine. 3. Relevance Feedback: . Example. Revised Results. Search Engine. 4. for Pseudo–Relevance Feedback . Yuanhua . Lv. . & . ChengXiang. . Zhai. Department of Computer Science, UIUC. Presented by Bo Man . 2014/11/18. Positional Relevance Model . for Pseudo–Relevance Feedback . Wang. CS@UVa. Explicit relevance feedback. 2. Updated. query. Feedback. Judgments:. d. 1 . . d. 2. -. d. 3 . …. d. k. -. .... Query. User . judgment. Retrieval. Engine. Document. collection. Results:. Inspirations, ideas . &. plans. Motivation. Ideal situation: general-purpose image annotation with unlimited vocabulary. Reality:. Classifiers with limited vocabulary and dependency on labeled training data. 1. 2. Take-. away. . today. Interactive relevance feedback:. improve initial retrieval results by telling the IR system which docs are relevant / . nonrelevant. Best known relevance feedback method: .