PPT-Pseudo-Relevance Feedback For Multimedia Retrieval

By Rong Yan Alexander G and Rong Jin Mwangi S Kariuki 200811629 Quiz Whats Negative PseudoRelevance feedback in multimedia retrieval Introduction As a result of high demand of content based access to video information. 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 . Paul N. Bennett, Microsoft Research. Joint with. Ece Kamar, Microsoft Research. Gabriella Kazai, Microsoft Research Cambridge. Motivation for Consensus Task. Recover actual . relevance of . a topic-document . CSC 575. Intelligent Information Retrieval. Intelligent Information Retrieval. 2. Retrieval Models. Model is an idealization or abstraction of an actual process. in this case, process is matching of documents with queries, i.e., retrieval. A New Family of Replacement Policies for Last-level Caches. Mainak. . Chaudhuri. Indian Institute of Technology, Kanpur. Agenda. Prolog. Configurations and Workloads. Fill Stack Order. Observations. 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. CSC . 575. Intelligent Information Retrieval. 2. Source: . Intel. How much information?. Google: . ~100 . PB a . day; 3+ million servers (15 . Exabytes. stored). Wayback Machine has . ~9 . PB + . 100 . INST 734. Doug . Oard. Module 13. Agenda. Image retrieval. Video retrieval. Multimedia retrieval. Multimedia. A set of time-synchronized modalities. Video. Images, object motion, camera motion, scenes. vs.. Weak Induction. Homework. Study Fallacies 1-18. Review pp. 103-132. Fallacies (definition § 4.1). § 4.2 Fallacies of Relevance (1 – 8). § 4.3 Fallacies of Weak Induction (9 – 14). For Next Class: pp. 139-152. Shachar. Lovett (IAS). Coding, Complexity and . Sparsity. workshop. August 2011. Overview. Pseudo-randomness – what? why?. Concrete examples. Local independence. Expander graphs. Small space computations. Tariff Administration, . August 2017. Purpose of . pseudo-tie procedure. Implement pseudo-ties in a reliable and compliant manner. Work with all stakeholders on necessary agreements and compensation. of . Non-coding Distributions . using . Stream Cipher Mechanism. Jeffrey Zheng. School of Software, Yunnan University . August 4, 2014. 2. nd. International Summit . on . Integrative . Biology. August . Lovett (IAS). Coding, Complexity and . Sparsity. workshop. August 2011. Overview. Pseudo-randomness – what? why?. Concrete examples. Local independence. Expander graphs. Small space computations. Inspirations, ideas . &. plans. Motivation. Ideal situation: general-purpose image annotation with unlimited vocabulary. Reality:. Classifiers with limited vocabulary and dependency on labeled training data. Tom Burton-West. Information Retrieval Programmer. Digital Library Production Service. University of Michigan Library. www.hathitrust.org/blogs/large-scale-search. Code4lib . February 12, 2013. w. www.hathitrust.org.