Please use this identifier to cite or link to this item: https://doi.org/10.1145/1991996.1992055
Title: Locally regressive G-optimal design for image retrieval
Authors: Zha, Z.-J. 
Zheng, Y.-T.
Wang, M. 
Chang, F.
Chua, T.-S. 
Keywords: active learning
image retrieval
optimum experimental design
relevance feedback
Issue Date: 2011
Source: Zha, Z.-J.,Zheng, Y.-T.,Wang, M.,Chang, F.,Chua, T.-S. (2011). Locally regressive G-optimal design for image retrieval. Proceedings of the 1st ACM International Conference on Multimedia Retrieval, ICMR'11. ScholarBank@NUS Repository. https://doi.org/10.1145/1991996.1992055
Abstract: Content Based Image Retrieval (CBIR) has attracted increasing attention from both academia and industry. Relevance Feedback is one of the most effective techniques to bridge the semantic gap in CBIR. One of the key research problems related to relevance feedback is how to select the most informative images for users to label. In this paper, we propose a novel active learning algorithm, called Locally Regressive G-Optimal Design (LRGOD) for relevance feedback image retrieval. Our assumption is that for each image, its label can be well estimated based on its neighbors via a locally regressive function. LRGOD algorithm is developed based on a locally regressive least squares model which makes use of the labeled and unlabeled images, as well as simultaneously exploits the local structure of each image. The images that can minimize the maximum prediction variance are selected as the most informative ones. We evaluated the proposed LRGOD approach on two real-world image corpus: Corel and NUS-WIDE-OBJECT [5] datasets, and compare it to three state-of-the-art active learning methods. The experimental results demonstrate the effectiveness of the proposed approach. © 2011 ACM.
Source Title: Proceedings of the 1st ACM International Conference on Multimedia Retrieval, ICMR'11
URI: http://scholarbank.nus.edu.sg/handle/10635/42183
DOI: 10.1145/1991996.1992055
Appears in Collections:Staff Publications

Show full item record
Files in This Item:
There are no files associated with this item.

SCOPUSTM   
Citations

2
checked on Dec 11, 2017

Page view(s)

59
checked on Dec 9, 2017

Google ScholarTM

Check

Altmetric


Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.