'Discriminative features' presentation slideshows

Transfer Learning for Image Classification

Transfer Learning for Image Classification. Transfer Learning Approaches. Leverage data from related tasks to improve performance: Improve generalization. Reduce the run-time of evaluating a set of classifiers. Two Main Approaches: Learning Shared Hidden Representations.

831 views • 69 slides

A Brief Overview of Trajectory Data Mining

Lunch Talk. A Brief Overview of Trajectory Data Mining. May 26, 2009 Jae-Gil Lee * This work was done while I was at University of Illinois at Urbana-Champaign for the years from 2006 through 2008. Outline. Introduction to Trajectory Data Clustering Classification Outlier Detection.

1.13k views • 54 slides

A Comprehensive Study on Third Order Statistical Features for Image Splicing Detection

A Comprehensive Study on Third Order Statistical Features for Image Splicing Detection. Xudong Zhao, Shilin Wang, Shenghong Li and Jianhua Li Shanghai Jiao Tong University, Shanghai P. R. China. Introduction. Digital Image Forensics: Active detection methods

363 views • 25 slides

From Tiger to Panda: Animal Head Detection

From Tiger to Panda: Animal Head Detection. Weiwei Zhang, Jian Sun, and Xiaoou Tang , Fellow, IEEE. Introdution. According to MSN image search statistics, the top five image search categories on the internet are Adult, Celebrity, News, Travel, and Animal.

398 views • 24 slides

Mid-level Visual Element Discovery as Discriminative Mode Seeking

Mid-level Visual Element Discovery as Discriminative Mode Seeking. Harley Montgomery 11/15/13. Main Idea . What are discriminative features exactly, and how can we find them automatically?

297 views • 18 slides

253 views • 13 slides

151 views • 13 slides

Elisa Fromont, Kick-off meeting, 14/02/2014

256 views • 10 slides

Tiled Convolutional Neural Networks

818 views • 1 slides

The model: takes HTML documents with embedded geolocation as input

A Hybrid Approach for Spotting , Disambiguating and Annotating Places in User-Generated Text Karen Stepanyan, George Gkotsis , Vangelis Banos *, Alexandra I. Cristea , Mike Joy Computer Science, University of Warwick, UK and

85 views • 1 slides

Online Selection of Discriminative Tracking Features

B. R+G+B. 2R+2G-B. Online Selection of Discriminative Tracking Features. By R. Collins and Y. Liu in ICCV 2003 Presenter: Roozbeh Mottaghi. Ranked based on a variance ratio. We would like log likelihood values of obj and bg: to be widely separated (high variance) AND

263 views • 1 slides