Paper Info
Title
Automatic image annotation for semantic image retrieval
Abstract
This paper addresses the challenge of automatic annotation of images for semantic image retrieval. In this research, we aim to identify visual features that are suitable for semantic annotation tasks. We propose an image classification system that combines MPEG-7 visual descriptors and support vector machines. The system is applied to annotate cityscape and landscape images. For this task, our analysis shows that the colour structure and edge histogram descriptors perform best, compared to a wide range of MPEG-7 visual descriptors. On a dataset of 7200 landscape and cityscape images representing real-life varied quality and resolution, the MPEG-7 colour structure descriptor and edge histogram descriptor achieve a classification rate of 82.8% and 84.6%, respectively. By combining these two features, we are able to achieve a classification rate of 89.7%. Our results demonstrate that combining salient features can significantly improve classification of images.
Year
DOI
Venue
2007
10.1007/978-3-540-76414-4_36
VISUAL
Keywords
Field
DocType
semantic image retrieval,classification rate,image classification system,mpeg-7 colour structure descriptor,automatic image annotation,visual feature,automatic annotation,edge histogram,colour structure,mpeg-7 visual descriptors,cityscape image,edge histogram descriptors,support vector machine,image annotation,image retrieval,image classification
Histogram,Computer vision,Automatic image annotation,Annotation,Cityscape,Pattern recognition,Computer science,Support vector machine,Image retrieval,Artificial intelligence,Contextual image classification,Visual Word
Conference
Volume
ISSN
ISBN
4781
0302-9743
3-540-76413-5
Citations 
PageRank 
References 
7
0.86
11
Authors
3
Name
Order
Citations
PageRank
Wenbin Shao1142.12
Golshah Naghdy2299.36
Son Lam Phung362548.64