Convex experimental design using manifold structure for image retrieval

Authors:
Lijun Zhang;Chun Chen;Wei Chen;Jiajun Bu;Deng Cai;Xiaofei He
Affiliations:
Zhejiang University, Hangzhou, China;Zhejiang University, Hangzhou, China;Zhejiang University, Hangzhou, China;Zhejiang University, Hangzhou, China;Zhejiang University, Hangzhou, China;Zhejiang University, Hangzhou, China
Venue:
MM '09 Proceedings of the 17th ACM international conference on Multimedia
Year:
2009

Citing 14
Cited 4

Content-Based Image Retrieval at the End of the Early Years

IEEE Transactions on Pattern Analysis and Machine Intelligence
Support vector machine active learning for image retrieval

MULTIMEDIA '01 Proceedings of the ninth ACM international conference on Multimedia
A Tutorial on Support Vector Machines for Pattern Recognition

Data Mining and Knowledge Discovery
Support vector machine active learning with applications to text classification

The Journal of Machine Learning Research
Improving image retrieval effectiveness in query-by-example environment

Proceedings of the 2003 ACM symposium on Applied computing
Convex Optimization

Convex Optimization
How to Complete Performance Graphs in Content-Based Image Retrieval: Add Generality and Normalize Scope

IEEE Transactions on Pattern Analysis and Machine Intelligence
Active learning via transductive experimental design

ICML '06 Proceedings of the 23rd international conference on Machine learning
Manifold Regularization: A Geometric Framework for Learning from Labeled and Unlabeled Examples

The Journal of Machine Learning Research
Laplacian optimal design for image retrieval

SIGIR '07 Proceedings of the 30th annual international ACM SIGIR conference on Research and development in information retrieval
Image retrieval: Ideas, influences, and trends of the new age

ACM Computing Surveys (CSUR)
trNon-greedy active learning for text categorization using convex ansductive experimental design

Proceedings of the 31st annual international ACM SIGIR conference on Research and development in information retrieval
Active learning with statistical models

Journal of Artificial Intelligence Research
Relevance feedback: a power tool for interactive content-based image retrieval

IEEE Transactions on Circuits and Systems for Video Technology

SED: supervised experimental design and its application to text classification

Proceedings of the 33rd international ACM SIGIR conference on Research and development in information retrieval
Locally regressive G-optimal design for image retrieval

Proceedings of the 1st ACM International Conference on Multimedia Retrieval
Column subset selection for active learning in image classification

Neurocomputing
Unsupervised face-name association via commute distance

Proceedings of the 20th ACM international conference on Multimedia

Quantified Score

Hi-index	0.00

Visualization

Abstract

Content Based Image Retrieval (CBIR) has become one of the most active research areas in computer science. Relevance feedback is often used in CBIR systems to bridge the semantic gap. Typically, users are asked to make relevance judgements on some query results, and the feedback information is then used to re-rank the images in the database. An effective relevance feedback algorithm must provide the users with the most informative images with respect to the ranking function. In this paper, we propose a novel active learning algorithm, called Convex Laplacian Regularized Ioptimal Design (CLapRID), for relevance feedback image retrieval. Our algorithm is based on a regression model which minimizes the least square error on the labeled images and simultaneously preserves the intrinsic geometrical structure of the image space. It selects the most informative images which minimize the average predictive variance. The optimization problem of CLapRID can be cast as a semidefinite programming (SDP) problem, and solved via interior-point methods. Experimental results on COREL database have demonstrate the effectiveness of the proposed algorithm for relevance feedback image retrieval.