From Bag of Categories to Tree of Object Recognition
Files
Date issued
2008
Authors
Journal Title
Journal ISSN
Volume Title
Publisher
Václav Skala - UNION Agency
Abstract
To recognize different category of objects, multiclass
categorization problem is often reduced to multiple binary
problems. Traditional approaches require training
different classifiers for each category. This can be
slow and the performance of learned single classifier is
poor for limited training samples. We present a multiclass
object recognition tree, in which the leaf node
and the non-leaf node correspond to one category and
a bag of categories, respectively. Each non-leaf node
captures the shared features of a bag of categories.
Each node also holds a group of classifiers trained by
AdaBoost, to discriminate the categories locating at its
left and right child node. Recognition is then a process
to find a path from the root to a leaf, which represents
a unique category. The very promising result on Caltech
101 dataset shows the robustness of the proposed
approach.
Description
Subject(s)
rozpoznávání objektů, multitřídní objektově rozhodovací strom, AdaBoost
Citation
WSCG '2008: Full Papers: The 16-th International Conference in Central Europe on Computer Graphics, Visualization and Computer Vision in co-operation with EUROGRAPHICS, University of West Bohemia Plzen, Czech Republic, February 4 - 7, 2008, p. 135-142.