Interactive tool for image annotation using a semi-supervised and hierarchical approach

被引：7
|
作者：
Chiang, Cheng-Chieh [1 ]
机构：
[1] Takming Univ Sci & Technol, Dept Informat Technol, Taipei 114, Taiwan
来源：
COMPUTER STANDARDS & INTERFACES | 2013年 / 35卷 / 01期
关键词：
Image annotation; Relevance feedback; Semantic gap; Semi-supervised clustering; COLOR;
D O I：
10.1016/j.csi.2012.05.002
中图分类号：
TP3 [计算技术、计算机技术];
学科分类号：
0812 ;
摘要：
This paper presents a semi-automatic tool, called IGAnn (Interactive image ANNotation), that assists users in annotating textual labels with images. IGAnn performs an interactive retrieval-like procedure: the system presents the user with images that have higher confidences, and then the user determines which images are actually relevant or irrelevant for a specified label. By collecting relevant and irrelevant images of iterations, a hierarchical classifier associated with the specified label is built using our proposed semi-supervised approach to compute confidence values of unlabeled images. This paper describes the system interface of IGAnn and also demonstrates quantitative experiments of our proposed approach. (C) 2012 Elsevier B.V. All rights reserved.
引用
收藏
页码：50 / 58
页数：9
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