PPT-Image Retrieval Discussion

Andrew Chi Brian Cristante COMP 790133 January 27 2015 Image Retrieval AI Vision Problem Systems Design Software Engineering Problem Sensory Gap What features should we use QueryDependent. 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 . 2014-03. 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. 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. 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. All slides ©Addison Wesley, 2008. Beyond Bag of Words. “Bag of Words”. a document is considered to be an unordered collection of words with no relationships. Extending representation. feature-based models. Divya Spandana . Marneni. Agenda. What is Big Data. Big Data and image processing. Why to analyze big images. Complexity involved in processing. Hadoop Image processing framework. Image Retrieval in big data.