Title :
An image retrieval method with multi-instance learning
Author :
Liqun ; Huangxinyuan
Author_Institution :
School of Information Science & Technology, Beijing Forestry University, China
Abstract :
In this paper, a multi-instance learning based CBIR (content-based image retrieval) approach is presented, and multi-instance learning method is applied in CBIR, in order to deal with the inherent ambiguity of images. First of all the whole image is regards as a multi-instance bag, secondly the image is partitioned into multi-regions by using adaptive k-means image segmentation method, and then query images posed by the user are transformed into corresponding positive and negative bags and a EM-DD(expectation maximization diverse density) algorithm is employed for image retrieval and relevance feedback. Finally, it makes the users get satisfying result.
Keywords :
Algorithm design and analysis; Feature extraction; Image color analysis; Image retrieval; Pixel; Shape; Training; Multiple-instance learning; content-based image retrieval; expectation maximization diverse density; relevance feedback;
Conference_Titel :
Information Science and Engineering (ICISE), 2010 2nd International Conference on
Conference_Location :
Hangzhou, China
Print_ISBN :
978-1-4244-7616-9
DOI :
10.1109/ICISE.2010.5690324