PPT-Multimodal Semantic Indexing for Image Retrieval

Author : pasty-toler | Published Date : 2016-10-13

P L Chandrika Advisors Dr C V Jawahar Centre for Visual Information Technology IIIT Hyderabad Problem Setting

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Multimodal Semantic Indexing for Image Retrieval: Transcript


P L Chandrika Advisors Dr C V Jawahar Centre for Visual Information Technology IIIT Hyderabad Problem Setting . IR Metrics. Latent Semantic Analysis. and Indexing. Crista. Lopes. Outline. Precision and Recall. The problem with indexing so far. Intuition for solving it. Overview of the solution. The Math. How to measure. 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. 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. CSC 575. Intelligent Information Retrieval. Intelligent Information Retrieval. 2. Indexing. Indexing is the process of transforming items (documents) into a searchable data structure. creation of document surrogates to represent each document. +. a (corny) ending. 5/4. An unexamined life . . is not worth living... . --Socrates. Mandatory blog . qns. Final on next Tuesday. . 9:50—11:40. Course Outcomes. After this course, you should be able to answer:. Yulia Kogan and . Ron . Shiff. 19.06.2016. References. J. Mao, W. Xu, Y. Yang, J. Wang, and A. L. Yuille. Explain images with multimodal recurrent neural networks. . arXiv preprint arXiv:1410.1090, 2014. 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?. Tutorial. Introduction. Miriam Fernandez | KMI, Open University, UK. Thanh Tran | Institute AIFB, KIT, DE. Peter Mika| Yahoo Research, Spain. Search . Document Retrieval vs. Data Retrieval. Differences of search technologies. Towards Bridging Semantic Gap and Intention Gap in Image Retrieval. Hanwang. Zhang. 1. , . Zheng. -Jun Zha. 2. , Yang Yang. 1. , . Shuicheng. Yan. 1. , . Yue. Gao. 1. , Tat-. Seng. Chua. 1. 1: National University of Singapore. 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. . and Indexing Models. CSC 575. Intelligent Information Retrieval. Information. need. Index. Pre-process. Parse. Collections. Rank. Query. text input. Lexical analysis and stop words. Result. Sets. How is. 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. Rosalia F. Tungaraza. Advisor: Prof. Linda G. Shapiro. Ph.D. Defense. Computer Science & Engineering. University of Washington. 1. Functional Brain Imaging. Study how the brain works . Imaging while subject performs a task .

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