PPT-Image and Video Retrieval

INST 734 Doug Oard Module 13 Agenda Image retrieval Video retrieval Multimedia retrieval Multimedia A set of timesynchronized modalities Video Images object motion camera motion scenes. R. ecordings in RDA. SEMLA Preconference. Nashville, Tennessee, October 9, 2013. Sonia Archer-Capuzzo, UNCG. smarcherdma@gmail.com. Sarah Hess Cohen, FSU. shcohen@admin.fsu.edu. Agenda: . Information + hands-on . David Meredith. dave@create.aau.dk. Aalborg University. Source. Chapter 16 of. Shiffman. , D. (2008). . Learning Processing. . Morgan Kaufmann. ISBN: 978-0-12-373602-4.. Overview. Displaying live video. Document Image Retrieval. David Kauchak. cs160. Fall 2009. adapted from. :. David . Doermann. http://terpconnect.umd.edu/~oard/teaching/796/spring04/slides/11/796s0411.ppt. Assign 4 . writeups. Overall, I was very happy. 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. 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.. Partly based on slides by Dmitri . Roduy. Topics. Data Types. Image Representation. Image/Video I/O. Matrix access. Image . Manipulation. Application: Bright Cars Detection in Video. Image Data Types. circulant. temporal encoding. CVPR 2013 . Oral. Outline. 1. . Introduction. 2. EVVE: an event retrieval . dataset. 3. Frame . description. 4. . Circulant. temporal . aggregation. 5. Indexing strategy and . 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?. (Paper ID: 2314). Vishwanath Saragadam,. . Aswin. . Sankaranarayanan. ,. Xin Li. 1. Compressive sensing. Solving underdetermined linear system of equations. Relies on sparsity of signal. Orthogonal Matching Pursuit. 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. Naifan Zhuang, Jun Ye, Kien A. Hua. Department of Computer Science. University of Central Florida. ICPR 2016. Presented by Naifan Zhuang. Motivation and Background. According to a report from Cisco, by 2019:. Video Wall Theme:. “Inform, Influence and Inspire”. Region 1. 3840 x 2160. Region 2. 1920 x 1830. Events,DateTime,Weather. 1920 x 330. Region 1. Video or static image . Resolution: 3840 x 2160. Video quality: 4k/Ultra HD. Adding content to the PresPrep software on your USB stick Step 1 - Move your image / video to your desktop Step 2 - Insert PresPrep USB into your computer and open it up Step 3 - Open up the folder