PPT-Training Image Classifiers with Similarity Metrics, Linear Programming, and Minimal Supervision

Asilomar SSC Karl Ni Ethan Phelps Katherine Bouman Nadya Bliss Lincoln Laboratory Massachusetts Institute of Technology 2 November 2012 This work is sponsored by the Department of the Air Force under Air Force contract FA872105C0002 Opinions interpretations conclusions and recommendations are those of the author and are not necessarily endorsed by the United States Government. . Schütze. and Christina . Lioma. Lecture . 14: Vector Space Classification. 1. Overview. Recap . . Feature selection. Intro vector space classification . . Rocchio. . kNN. Linear classifiers. Li, Senior Member, IEEE,. Linfeng. . Xu. , Member, IEEE, and . Guanghui. Liu. Face Hallucination via Similarity Constraints. Outline. Introduction. Proposed Method. Framework of the Proposed Method. 1. 3.3 Implementation. (1) naive implementation. (2) revised simplex method. (3) full tableau implementation. (1) Naive implementation :. Given basis . . Compute . ( solve . ). Choose . such that . Usman Roshan. CS 675. Comparison of classifiers. Empirical comparison of supervised classifiers – ICML 2006. Do we need hundreds of classifiers – JMLR 2014. Empirical comparison of supervised classifiers – ICML 2006 . 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. Linear classifiers on pixels are bad. Solution 1: Better feature vectors. Solution 2: Non-linear classifiers. A pipeline for recognition. Compute image gradients. Compute SIFT descriptors. Assign to k-means centers. Juri Minxha. Medical Image Analysis. Professor Benjamin Kimia. Spring 2011. Brown University. Review of Registration. . . Similarity Metric Optimization. 1. Similarity Metric. Mutual Information, Cross-Correlation, Correlation Ratio,. (Paul Viola , Michael Jones . ). Bibek. Jang . Karki. . Outline. Integral Image. Representation of image in summation format. AdaBoost. Ranking of features. Combining best features to form strong classifiers. for Indoor Room Recognition . CGS participation at ImageCLEF2010 Robot Vision Task . Walter . Lucetti. . Emanuel . Luchetti. . Gustavo Stefanini . Advanced . Robotics Research Center Scuola Superiore di Studi e Perfezionamento Sant’Anna . Chapters . 18.5-18.12; 20.2.2. Decision Regions and Decision Boundaries. Classifiers:. Decision trees. K-nearest neighbors. Perceptrons. Support . vector Machines (SVMs), Neural . Networks. Naïve . Bayes. Problems and Solutions. Classifying based . on similarities. :. 2. Van Gogh. Or. Monet. ?. Van Gogh. Monet. the Similarity-based Classification Problem. 3. (painter). (paintings). the Similarity-based Classification Problem. Spring . 2018. Sungsoo. Park. Linear Programming 2018. 2. Instructor . Sungsoo. Park (room 4112, . sspark@kaist.ac.kr. , . tel:3121. ). Office hour: Mon, Wed 14:30 – 16:30 or by appointment. Classroom: E2-2 room 1120. understand and implement editorial opportunities and stunts across syndicated channels Monitor competitive programming and marketplace trends and analyze their implications Linear Classifiers. Mark Hasegawa-Johnson, 3/2020. Including Slides by . Svetlana Lazebnik, 10/2016. License: CC-BY 4.0. Linear Classifiers. Classifiers. Perceptron. Linear classifiers in general. Logistic regression.