Automatic image annotation using semi-supervised generative modeling

doi:10.1016/j.patcog.2014.07.012

Return

Automatic image annotation using semi-supervised generative modeling

2015-01-01

PRE

S. Hamid Amiri

Mansour Jamzad *

DOI：10.1016/j.patcog.2014.07.012

Original

Original request for help

Save

Abstract

En 中文

Image annotation approaches need an annotated dataset to learn a model for the relation between images and words. Unfortunately, preparing a labeled dataset is highly time consuming and expensive. In this work, we describe the development of an annotation system in semi-supervised learning framework which by incorporating unlabeled images into training phase reduces the system demand to labeled images. Our approach constructs a generative model for each semantic class in two main steps. First, based on Gamma distribution, a generative model is constructed for each semantic class using labeled images in that class. The second step incorporates the unlabeled images by using a modified EM algorithm to update parameters of the constructed generative models. Performance evaluation of the proposed method on a standard dataset reveals that using unlabeled images will result in considerable improvement in accuracy of the annotation systems when a limited number of labeled images for each semantic class are available. (C) 2014 Elsevier Ltd. All rights reserved.

Keywords:

Image annotation

Semi-supervised learning

Generative modeling

Gamma distribution

AI Summary

Key information extracted from the uploaded paper, including a brief overview, abstract, background, key highlights, visual analysis, and future outlook.