Indexed by:期刊论文

Date of Publication:2018-11-06

Journal:PLOS ONE

Included Journals:PubMed、SCIE、Scopus

Volume:13

Issue:11

Page Number:e0206971

ISSN No.:1932-6203

Key Words:article; diagnostic test accuracy study; image retrieval; support vector machine; visual attention

Abstract:Automatic image annotation not only has the efficiency of text-based image retrieval but also achieves the accuracy of content-based image retrieval. Users of annotated images can locate images they want to search by providing keywords. Currently most automatic image annotation algorithms do not consider the relative importance of each region in the image, and some algorithms extract the image features as a whole. This makes it difficult for annotation words to reflect salient versus non-salient areas of the image. Users searching for images are usually only interested in the salient areas. We propose an algorithm that integrates a visual attention mechanism with image annotation. A preprocessing step divides the image into two parts, the salient regions and everything else, and the annotation step places a greater weight on the salient region. When the image is annotated, words relating to the salient region are given first. The support vector machine uses particle swarm optimization to annotate the images automatically. Experimental results show the effectiveness of the proposed algorithm.