PPT-Special Topic on Image Retrieval

201403 Popular Visual Features Global feature Color correlation histogram Shape context GIST Color name Local feature Detector DoG MSER Hessian Affine KAZE FAST Descriptor SIFT SURF LIOP. 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. with Time Series Query. Hyun . Duk. . Kim (now at Twitter) , . Danila. . Nikitin. (now at Google), . ChengXiang. . Zhai. University of Illinois at Urbana-Champaign. Malu. Castellanos, . Meichun. 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 . 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?. Location-aware mobile applications development. Spring 2011. Paras Pant. Overview. Introduction. Basic Image Analysis. Content-Based Image Retrieval. Some location based system. . Introduction. Nowadays, the analysis of information has become paramount importance. . relative importance of objects in image retrieval. 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. Nikhil . Rasiwasia. , . Nuno. . Vasconcelos. Statistical Visual Computing Laboratory. University of California, San Diego. Thesis Defense. Ill pause for a few moments so that you all can finish reading this. . 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.