Title: Study on image retrieval system base on multi-objective and multi-instance learning

Authors: Ke Chen; Zhiping Peng; Wende Ke

Addresses: Department of Computer Science and Technology, Guangdong University of Petrochemical Technology, 525000, Guangdong, China ' Department of Computer Science and Technology, Guangdong University of Petrochemical Technology, 525000, Guangdong, China ' Department of Computer Science and Technology, Guangdong University of Petrochemical Technology, 525000, Guangdong, China

Abstract: In this paper, the multi-instance learning algorithm is improved under the image retrieval framework based on contents, and the improved multi-instance learning algorithm is applied to image retrieval to better handle the ambiguity of the image. In this method, the image is used as the multi-instance bag and is divided into multiple instances by image segmentation algorithm, and then the multi-instance learning is performed with the multi-objective-diverse-density algorithm. The learning results are ordered by image similarity using the vector space model. Finally, relevant feedback is given in accordance with the positive bag and negative bag chosen by the user to provide satisfactory results to the user.

Keywords: multi-instance learning; multi-objective learning; image retrieval; content; EM-DD; MO-DD; image ambiguity; image similarity; vector space model.

DOI: 10.1504/IJWMC.2013.054045

International Journal of Wireless and Mobile Computing, 2013 Vol.6 No.2, pp.158 - 164

Received: 01 Sep 2012
Accepted: 25 Oct 2012

Published online: 15 May 2013 *

Full-text access for editors Access for subscribers Purchase this article Comment on this article