期刊名称：Lecture Notes in Engineering and Computer Science
印刷版ISSN：2078-0958
电子版ISSN：2078-0966
出版年度：2018
卷号：2233&2234
页码：293-298
出版社：Newswood and International Association of Engineers
摘要：The semantic gap presents an arduous task in
semantic-based image retrieval investigations. In this paper, the
author proposes the AdaBoost learning algorithm for large
vocabulary classification. The main finding of this investigation
is that using Gentle AdaBoost for image classification produced
excellent results in terms of precision and the F-measure. With
190 concrete keywords categorisation, AdaBoost renders more
keywords assignable and allows a significant improvement in all
accuracy measures: precision, recall and F-measure. An
AdaBoost vs. SVMs comparison showed that AdaBoost was an
effective classifier using the one-versus-the-rest mode of
operation.
