PDF-Comparing discriminating transformations and SVM for learning during multimedia retrieval

Figure 1 A picture is worth a thousand words different users at different times can be interested in either the. a 12 22 a a mn is an arbitrary matrix Rescaling The simplest types of linear transformations are rescaling maps Consider the map on corresponding to the matrix 2 0 0 3 That is 7 2 0 0 3 00 brPage 2br Shears The next simplest type of linear transfo Ch. 2 Lesson 3. Pg. 123. What will you will learn?. Enlarge Photographs. Make something from a pattern. Identify Similarity. Two figures are . similar. if the second can be obtained from the first by a sequence of transformations and dilations. Lecture 3. Jitendra. Malik. Pose and Shape. Rotations and reflections are examples. of orthogonal transformations . Rigid body motions. (Euclidean transformations / . isometries. ). Theorem:. Any rigid body motion can be expressed as an orthogonal transformation followed by a translation.. 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.. 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. Arvind. . Balasubramanian. arvind@utdallas.edu. Multimedia . Lab (ECSS 4.416). The University of Texas at Dallas. Me and My Research. Research Interests: . Machine Learning. Data Mining. Statistical Analysis. Maurice J. . Chacron. and Kathleen E. Cullen. Outline. Lecture 1: . - Introduction to sensorimotor . . transformations. - . The case of “linear” sensorimotor . transformations: . Week 10 . Presented by Christina Peterson. Movement Exemplar-SVMs . Tran and . Torresani. [1] based the MEX-SVM on the work of . Malisiewicz. . et. al. . [2]. Linear SVMs applied to histograms of space-time interest points (STIPs) calculated from . Arvind. . Balasubramanian. arvind@utdallas.edu. Multimedia Lab. The University of Texas at Dallas. Me and My Research. Research Interests: . Machine Learning. Data Mining. Statistical Analysis. Applications of the above in Multimedia. Information Retrieval. Information Retrieval. Konsep. . dasar. . dari. IR . adalah. . pengukuran. . kesamaan. sebuah. . perbandingan. . antara. . dua. . dokumen. , . mengukur. . sebearapa. . Tommy Gober, MS. LeTourneau University. Rich Mayer, PhD. Professor of Psychology. University of California – Santa Barbara. Research science of learning. Father of “Multimedia Learning Theory”. Graph: .  . What is the parent function for this graph?. What does the parent function look like?. Shape is a V. Vertex is (0, 0). Slope is 1, opens up. How is the graph above different from the parent function?. Vapnik. Good empirical results. Non-trivial implementation. Can be slow and memory intensive. Binary classifier. Was the big wave before graphical models and then deep learning, important part of your knowledge base. Presented by: . Xuwen. Zhao. Overview. Why we need this prediction. Algorithms used. SVM (support vector machine). RFE (recursive feature elimination). 3 different conditions to test for accuracies .