An EM based multiple instance learning method for image classification

Hsiao-Tien Pao*, S. C. Chuang, Y. Y. Xu, Hsin Chia Fu

*此作品的通信作者

研究成果: Article同行評審

33 引文 斯高帕斯(Scopus)

摘要

In this paper, we propose an EM based learning algorithm to provide a comprehensive procedure for maximizing the measurement of diverse density on given multiple Instances. Furthermore, the new EM based learning framework converts an MI problem into a single-instance treatment by using EM to maximize the instance responsibility for the corresponding label of each bag. To learn a desired image class, a user may select a set of exemplar images and label them to be conceptual related (positive) or conceptual unrelated (negative) images. A positive image consists of at least one object that the user may be interested, and a negative image should not contain any object that the user may be interested. By using the proposed EM based learning algorithm, an image retrieval prototype system is implemented. Experimental results show that for only a few times of relearning cycles, the prototype system can retrieve user's favor images from WWW over Internet.

原文American English
頁(從 - 到)1468-1472
頁數5
期刊Expert Systems with Applications
35
發行號3
DOIs
出版狀態Published - 10月 2008

指紋

深入研究「An EM based multiple instance learning method for image classification」主題。共同形成了獨特的指紋。

引用此