Content-based image retrieval (CBIR) has attracted substantial attention during the past few years for its potential applications. To bridge the gap between low level visual features and high level semantic concepts, various relevance feedback (RF) or interactive re-ranking (IR) schemes have been designed to improve the performance of a CBIR system. In this paper, we propose a novel subspace learning based IR scheme by using a graph embedding framework, termed Laplacian Regularized Subspace Learning (LRSL). The LRSL method can model both within-class compactness and between-class separation by specially designing an intrinsic graph and a penalty graph in the graph embedding framework, respectively. In addition, LRSL can share the popular assumption of the biased discriminant analysis (BDA) for IR but avoid the singular problem in BDA. Extensive experimental results have shown that the proposed LRSL method is effective for reducing the semantic gap and targeting the intentions of users for an image retrieval task.
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