Neural Computation
Support vector machine active learning for image retrieval
MULTIMEDIA '01 Proceedings of the ninth ACM international conference on Multimedia
Support vector machine active learning with applications to text classification
The Journal of Machine Learning Research
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
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
Ranking with local regression and global alignment for cross media retrieval
MM '09 Proceedings of the 17th ACM international conference on Multimedia
Beyond distance measurement: constructing neighborhood similarity for video annotation
IEEE Transactions on Multimedia - Special section on communities and media computing
MSRA-MM 2.0: A Large-Scale Web Multimedia Dataset
ICDMW '09 Proceedings of the 2009 IEEE International Conference on Data Mining Workshops
Laplacian regularized D-optimal design for active learning and its application to image retrieval
IEEE Transactions on Image Processing
Semi-Supervised Learning
Active learning in multimedia annotation and retrieval: A survey
ACM Transactions on Intelligent Systems and Technology (TIST)
LIBSVM: A library for support vector machines
ACM Transactions on Intelligent Systems and Technology (TIST)
IEEE Transactions on Multimedia
Hi-index | 0.01 |
In this paper, we propose a novel active learning algorithm, called Locally Regressive Optimal Design (LROD), to improve the effectiveness of relevance feedback-based social image retrieval. Our algorithm assumes that for each data point, the label values of both this data point and its neighbors can be well estimated using a locally regressive function. Specifically, we adopt a local linear regression model to predict the label value of each data point in a local patch. The regularized local model predication error of the local patch is defined as our local loss function. Then, a unified objective function is proposed to minimize the summation of these local loss functions over all the data points, so that an optimal predicated label value can be assigned to each data point. Finally, we embed it into a semi-supervised learning framework to construct the final objective function. Experiment results on MSRA-MM2.0 database demonstrate the efficiency and effectiveness of the proposed algorithm for relevance feedback-based social image retrieval.