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Spectral regression: a unified subspace learning framework for content-based image retrieval

Relevance feedback is a well established and effective framework for narrowing down the gap between low-level visual features and high-level semantic concepts in content-based image retrieval. In most of traditional implementations of relevance feedback, a distance metric or a classifier is usually learned from user's provided negative and positive examples. However, due to the limitation of the user's feedbacks and the high dimensionality of the feature space, one is often confront with the issue of the curse of the dimensionality. Recently, several researchers have considered manifold ways to address this issue, such as Locality Preserving Projections, Augmented Relation Embedding, and Semantic Subspace Projection. In this paper, by using techniques from spectral graph embedding and regression, we propose a unified framework, called spectral regression, for learning an image subspace. This framework facilitates the analysis of the differences and connections between the algorithms mentioned above. And more crucially, it provides much faster computation and therefore makes the retrieval system capable of responding to the user's query more efficiently.