PPT-Evaluation of Image Retrieval Results

Author : conchita-marotz | Published Date : 2017-06-05

Relevant images which meet users information need Irrelevant images which dont meet users information need Query cat Relevant Irrelevant 1 Accuracy Given a query

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Evaluation of Image Retrieval Results: Transcript

Relevant images which meet users information need Irrelevant images which dont meet users information need Query cat Relevant Irrelevant 1 Accuracy Given a query an engine classifies each image as Relevant or . 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. Pattern Completion and Recapitulation. Episodic Retrieval and the Frontal Lobes. Cues for Retrieval. The Second Time Around: Recognizing Stimuli by Recollection and Familiarity. Misremembering the Past. 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 . Date :. . 2012 . / . 04. . / . 12. 資訊碩一 . 10077034. 蔡勇儀 . @. . LAB603 . Outline. Introduction. Preliminaries. Method. Experimental result. Conclusions. Introduction. Image retrieval have more challenge than text retrieval.. . P . L . Chandrika. . . Advisors: Dr.. . C. V. Jawahar . . . Centre for Visual Information Technology, IIIT- Hyderabad. Problem Setting . Group 3. Chad Mills. Esad Suskic. Wee Teck Tan. Outline. System and Data. Document Retrieval. Passage Retrieval. Results. Conclusion. System and Data. Development. Testing. TREC 2004. TREC 2004. TREC 2005. and Re-ranking. Ling573. NLP Systems and Applications. May 3, 2011. Upcoming Talks. Edith Law. Friday: 3:30; CSE 303. Human Computation: Core Research Questions and Opportunities . Games with a purpose, . Cristiano Chesi . NETS. , IUSS Center . for . Ne. urocognition and . T. heoretical . S. yntax - Pavia. IGG 40. Università di Trento. Outline. Complexity in Object(-headed) Relative Clauses (ORs). Memory-load accounts. (for MODIS). Andy Harris. Jonathan . Mittaz. Prabhat. . Koner. (Chris Merchant, Pierre . LeBorgne. ). Satellite data – pros and cons. Main advantages of satellite data. Frequent and regular global coverage (cloud cover permitting for IR). Sung . Ju. Hwang and Kristen . Grauman. University of Texas at Austin. Image retrieval. Query image. Image Database. Image 1. Image 2. Image k. Content-based retrieval from an image database. …. Relative importance of objects. Information Retrieval. Information Retrieval. Konsep. . dasar. . dari. IR . adalah. . pengukuran. . kesamaan. sebuah. . perbandingan. . antara. . dua. . dokumen. , . mengukur. . sebearapa. . ChengXiang. (“Cheng”) . . Zhai. Department of Computer Science. University of Illinois at Urbana-Champaign. http://www.cs.uiuc.edu/homes/czhai. . Email: czhai@illinois.edu. 1. Yahoo!-DAIS Seminar, UIUC. Through Online Experimentation. WSDM Workshop on Web Search Click Data. February 12. th. , 2012. Yisong Yue. Carnegie Mellon University. Offline Post-hoc Analysis. Launch some ranking function on live traffic. BY. DR. ADNAN ABID. Lecture # . Introduction. Library Management System. Structured Data Storage / Tables. Semi-Structured and Unstructured . Employee Department Salary. Library Digitization. Information Retrieval Models.

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