Query Sub-manifold Biased Semi-supervised Algorithm for Image Retrieval
Under the circumstance of the relevance-feedback-based image retrieval,the quantity of relevant and irrelevant images labeled by user is very few leading to a canonical small-size sample problem.The intrinsic structure of such image data usually appears in the manifold form in lower-dimension space,thus the goal of the manifold-regularization is to utilize the structure to make the optimal solution able to reflect the distribution geometry of the data.With the aim to solving such small-size sample problem,a new semi-supervised image retrieval algorithm is proposed in this paper,which based on the manifold-regularization framework.In the proposed algorithm,the term of manifold-regularization only depends on the query submanifold rather than the global structure of the whole data set.The experimental results on two image databases indicate that such new retrieval algorithm is more effective than four current state-of-the-art algorithms.