Year: 2008
Type: conference-paper
Abstract: To recognize different category of objects, multiclass categorization problem is often reduced to multiple bi-nary problems. Traditional approaches require train-ing 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 mul-ticlass 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 Cal-tech 101 dataset shows the robustness of the proposed approach. Keywords object recognition, bag of categories, multiclass object recognition tree, AdaBoost 1.
Authors: Haijing Wang, Tianwen Zhang, Peihua Li
Language: English
Subfield: Computer Vision and Pattern Recognition
Field: Computer Science
Domain: Physical Sciences
Open Access status: green
OpenAlex ID: W2793047705