Paper Info
Title
Semi-Automatic Image Annotation
Abstract
A novel approach to semi-automatically and progressively annotating images with keywords is presented. The progressive annotation process is embedded in the course of integrated keyword-based and content-based image retrieval and user feedback. When the user submits a keyword query and then provides relevance feedback, the search keywords are automatically added to the images that receive positive feedback and can then facilitate keyword-based image retrieval in the future. The coverage and quality of image annotation in such a database system is improved progressively as the cycle of search and feedback increases. The strategy of semi-automatic image annotation is better than manual annotation in terms of efficiency and better than automatic annotation in terms of accuracy. A performance study is presented which shows that high annotation coverage can be achieved with this approach, and a preliminary user study is described showing that users view annotations as important and will likely use them in image retrieval. The user study also suggested user interface enhancements needed to support relevance feedback. We believe that similar approaches could also be applied to annotating and managing other forms of multimedia objects.
Year
Venue
Keywords
2001
HUMAN-COMPUTER INTERACTION - INTERACT'01
image annotation, image retrieval, relevance feedback, image database, user study, performance evaluation
Field
DocType
Citations 
Annotation,Automatic image annotation,Relevance feedback,Information retrieval,Computer science,Manual annotation,Image retrieval,User interface,Visual Word
Conference
62
PageRank 
References 
Authors
7.31
10
6
Name
Order
Citations
PageRank
Liu Wenyin12531215.13
Susan Dumais2139482130.47
Yanfeng Sun3627.31
Hong-Jiang ZHANG4173781393.22
Mary Czerwinski55028421.65
Brent A. Field6627.31