Object categorization using collections of parts and second order pooling features

This thesis presents an investigation of the Collection of Parts Model for object categorization. Multiclass categorization is performed using the Collections of Parts model. Results using Support Vector Machines, L1 Logistic Regression and Boosted Decision Trees are presented and discussed. Methods to analyze confusion in these results are developed and results are presented. The Collections of Parts model is augmented with features from features generated by Second Order Pooling resulting in a significant improvement in performance.

Name thesis:degree_name

M.S.

Level thesis:degree_level

Thesis

Discipline thesis:degree_discipline

Computer Science

Grantor

University of Illinois at Urbana-Champaign

Year dc:date

2013

Author dc:creator

• Sukumar, Pritam

Contributors dc:contributor

• Hoiem, Derek W.

• computer vision
• machine learning
• object categorization

Statement dc:rights

• Copyright 2013 Pritam Sukumar

Language dc:language

en

Handle dc:identifier

http://hdl.handle.net/2142/45296

OAI identifier oai:identifier

oai:www.ideals.illinois.edu:2142/45296