Title

Probabilistic Label Trees For Efficient Large Scale Image Classification

Keywords

image classification; label tree; large-scale recognition; maximum likelihood estimation

Abstract

Large-scale recognition problems with thousands of classes pose a particular challenge because applying the classifier requires more computation as the number of classes grows. The label tree model integrates classification with the traversal of the tree so that complexity grows logarithmically. In this paper, we show how the parameters of the label tree can be found using maximum likelihood estimation. This new probabilistic learning technique produces a label tree with significantly improved recognition accuracy. © 2013 IEEE.

Publication Date

11-15-2013

Publication Title

Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition

Number of Pages

843-850

Document Type

Article; Proceedings Paper

Personal Identifier

scopus

DOI Link

https://doi.org/10.1109/CVPR.2013.114

Socpus ID

84887356989 (Scopus)

Source API URL

https://api.elsevier.com/content/abstract/scopus_id/84887356989

This document is currently not available here.

Share

COinS