'Metric learning' presentation slideshows

Locality Sensitive Hashing and Large Scale Image Search

Locality Sensitive Hashing and Large Scale Image Search. Yunchao Gong UNC Chapel Hill yunchao@cs.unc.edu. The problem. Large scale image search: We have a candidate image Want to search a large database to find similar images Search the internet to find similar images Fast Accurate .

1.75k views • 55 slides

Distance Metric Learning: A Comprehensive Survey

Distance Metric Learning: A Comprehensive Survey. Liu Yang Advisor: Rong Jin May 8th, 2006. Outline. Introduction Supervised Global Distance Metric Learning Supervised Local Distance Metric Learning Unsupervised Distance Metric Learning Distance Metric Learning based on SVM

544 views • 51 slides

Human Action Recognition using Spatio-Temporal Classification

Human Action Recognition using Spatio-Temporal Classification. ??? Ching-Hsien Fang 98.10.14 func1115@csie.ncku.edu.tw. Outline. Introduction Flowchart Learning for Spatio-Temporal Classification 3.1 Spatial Subspace Creation using Locality Preserving Projection(LPP)

613 views • 31 slides

A Survey on Distance Metric Learning (Part 2)

452 views • 30 slides

A Survey on Distance Metric Learning (Part 2)

303 views • 30 slides

Fine-Grained Visual Comparisons with Local Learning

419 views • 27 slides

Random Forest for Metric Learning with Pairwise Position Dependence

Random Forest for Metric Learning with Pairwise Position Dependence. Caiming Xiong, David Johnson, Ran Xu , Jason J. Corso Department of Computer Science and Engineering SUNY at Buffalo { cxiong , davidjoh , rxu2, jcorso}@buffalo.edu Presented at ACM SIG KDD 2012 in Beijing, China.

457 views • 23 slides

A Novel Local Patch Framework for Fixing Supervised Learning Models

A Novel Local Patch Framework for Fixing Supervised Learning Models. Yilei Wang 1 , Bingzheng Wei 2 , Jun Yan 2 , Yang Hu 2 , Zhi-Hong Deng 1 , Zheng Chen 2. 1 Peking University 2 Microsoft Research Asia. Outline. Motivation & Background Problem Definition & Algorithm Overview

342 views • 23 slides

Semi-Supervised Hierarchical Models for 3D Human Pose Reconstruction

Semi-Supervised Hierarchical Models for 3D Human Pose Reconstruction. Atul Kanaujia, CBIM, Rutgers Cristian Sminchisescu, TTI-C Dimitris Metaxas,CBIM, Rutgers. 3D Human Pose Inference Difficulties Towards automatic monocular methods. Background clutter Geometric transforms

368 views • 21 slides

Integrating Constraints and Metric Learning in Semi-Supervised Clustering

Integrating Constraints and Metric Learning in Semi-Supervised Clustering. Mikhail Bilenko, Sugato Basu, Raymond J. Mooney ICML 2004 Presented by Xin Li. Semi-Supervised Clustering. K=4. Semi-Supervised Clustering. Semi-Supervised Clustering. How to exploit supervision in clustering.

385 views • 21 slides

Integrating Constraints and Metric Learning in Semi-Supervised Clustering

Integrating Constraints and Metric Learning in Semi-Supervised Clustering. Mikhail Bilenko, Sugato Basu, Raymond J. Mooney ICML 2004 Presented by Xin Li. Semi-Supervised Clustering. K=4. Semi-Supervised Clustering. Semi-Supervised Clustering. How to exploit supervision in clustering.

383 views • 21 slides

Nonlinear Adaptive Distance Metric Learning for Clustering

Nonlinear Adaptive Distance Metric Learning for Clustering. Jianhui Chen, Zheng Zhao, Jieping Ye, Huan Liu KDD, 2007 Reported by Wen-Chung Liao, 2010/01/19. Outlines. Motivation Objective ADAPTIVE DISTANCE METRIC LEARNING: THE LINEAR CASE

350 views • 16 slides

Multiple Parameter Selection of Support Vector Machine

349 views • 12 slides

Good recognition is non-metric

165 views • 8 slides