PPT-Evaluation of Image Retrieval Results

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 . David Kauchak. cs458. Fall . 2012. Empirical Evaluation of Dissimilarity Measures for Color and Texture. Jan . Puzicha. , Joachim M. . Buhmann. , . Yossi. . Rubner. & Carlo . Tomasi. Image processing. David Kauchak. cs160. Fall . 2009. Empirical Evaluation of Dissimilarity Measures for Color and Texture. Jan . Puzicha. , Joachim M. . Buhmann. , . Yossi. . Rubner. & Carlo . Tomasi. Administrative. Outline. Research . in Image Processing and Computer Vision. Finding Images. Content-based Image Retrieval. Find Images With Similar Colors. Find Images with Similar Shape. Goal: Find Images with Similar Content. 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. Petr Doubek, Jiri Matas, Michal Perdoch and Ondrej Chum. Center. for Machine Perception, Czech Technical University in Prague, Czech Republic. Detection of repetitive patterns in images is a well-established computer vision problem. However, the detected patterns are rarely used in any application. A method for representing a lattice or line pattern by shift-invariant descriptor of the repeating tile is presented. The descriptor respects the inherent shift ambiguity of the tile definition and is robust to viewpoint change. Repetitive structure matching is demonstrated in a retrieval experiment where images of buildings are retrieved solely by repetitive patterns.. 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.. 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. . P . L . Chandrika. . . Advisors: Dr.. . C. V. Jawahar . . . Centre for Visual Information Technology, IIIT- Hyderabad. Problem Setting . The Briefest Presentation by. N. . Kharma. & A. . Mazhurin. CellNetQL. Task. Given an image, partially segmented, by hand. . Automatically create a machine which would segment the rest of the image. 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?. Tzachi. . Hershkovich. Image Quality – Degradation sources. Full Reference-Image Quality Assessment vs. No . Reference-Image Quality Assessment. System architecture. Training. Evaluation and results. 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. 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. rsay. Valorization. of . research. . results. Speaker : . S. KAMARA. Contributors. . : . Laboratory. . Valorization. Correspondants (. CVLs. ). CSNSM. IMNC. IPNO. LAL. LPT. 14-17 . january. 2019.