Label-Specific Training Set Construction from Web Resource for Image Annotation

TL;DR

This paper introduces a semi-automatic method to build more accurate training sets from web images for image annotation, improving label quality and annotation accuracy.

Contribution

It presents a novel framework for constructing label-specific training sets from web resources, enhancing image annotation performance.

Findings

Constructed training sets lead to higher annotation accuracy.

Framework effectively filters noisy web tags.

Experimental results validate the approach's effectiveness.

Abstract

Recently many research efforts have been devoted to image annotation by leveraging on the associated tags/keywords of web images as training labels. A key issue to resolve is the relatively low accuracy of the tags. In this paper, we propose a novel semi-automatic framework to construct a more accurate and effective training set from these web media resources for each label that we want to learn. Experiments conducted on a real-world dataset demonstrate that the constructed training set can result in higher accuracy for image annotation.