Active learning for social image retrieval using Locally Regressive Optimal Design

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.