Electrical Engineering
      and Computer Sciences

Electrical Engineering and Computer Sciences

COLLEGE OF ENGINEERING

UC Berkeley

Selecting Shape Features Using Multi-class Relevance Vector Machine

Hao Zhang and Jitendra Malik

EECS Department
University of California, Berkeley
Technical Report No. UCB/EECS-2005-6
October 10, 2005

http://www.eecs.berkeley.edu/Pubs/TechRpts/2005/EECS-2005-6.pdf

The task of visual object recognition benefits from feature selection as it reduces the amount of computation in recognizing a new instance of an object, and the selected features give insights into the classification process. We focus on a class of current feature selection methods known as embedded methods: due to the nature of multi-way classification in object recognition, we derive an extension of the Relevance Vector Machine technique to multi-class. In experiments, we apply Relevance Vector Machine on the problem of digit classification and study its effects. Experimental results show that our classifier enhances accuracy, yields good interpretation for the selected subset of features and costs only a constant factor of the baseline classifier.


BibTeX citation:

@techreport{Zhang:EECS-2005-6,
    Author = {Zhang, Hao and Malik, Jitendra},
    Title = {Selecting Shape Features Using Multi-class Relevance Vector Machine},
    Institution = {EECS Department, University of California, Berkeley},
    Year = {2005},
    Month = {Oct},
    URL = {http://www.eecs.berkeley.edu/Pubs/TechRpts/2005/EECS-2005-6.html},
    Number = {UCB/EECS-2005-6},
    Abstract = {The task of visual object recognition benefits from feature selection as it reduces the amount of computation in recognizing a new instance of an object, and the selected features give insights into the classification process. 
We focus on a class of current feature selection methods known as embedded methods:
due to the nature of multi-way classification in object recognition, we derive an extension of the Relevance Vector Machine technique to multi-class.
In experiments, we apply Relevance Vector Machine on the problem of digit classification and study its effects. 
Experimental results show that our classifier enhances accuracy, yields good interpretation for the selected subset of features and costs only a constant factor of the baseline classifier.}
}

EndNote citation:

%0 Report
%A Zhang, Hao
%A Malik, Jitendra
%T Selecting Shape Features Using Multi-class Relevance Vector Machine
%I EECS Department, University of California, Berkeley
%D 2005
%8 October 10
%@ UCB/EECS-2005-6
%U http://www.eecs.berkeley.edu/Pubs/TechRpts/2005/EECS-2005-6.html
%F Zhang:EECS-2005-6