As a powerful tool for content-based image retrieval, many techniques have been proposed for relevance feedback. A non-negative matrix factorisation (NMF)-based relevance feedback approach is introduced. This approach uses a standard NMF algorithm to construct a reliable semantic space from a pool of relevant images based on a user's interactions, because the latent semantic space derived by NMF does not need to be orthogonal, and each image is guaranteed to take only non-negative values in all the latent semantic directions. It means that each axis in the space derived by NMF has a straightforward correspondence with each image semantic class. In addition, the hidden semantic features of the query and images in the database are extracted with an NMF-projecting algorithm. By memorising the feedback information provided by the user, the knowledge accumulated from past relevance interaction is used to update semantic space, which results in the semantic space being closer to the user's expectation. The experiments show that the proposed NMF-based relevance feedback approach performs better than other relevance feedback approaches.
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