Kernel PCA-based semantic feature estimation approach for similar image retrieval

A kernel PCA-based semantic feature estimation approach for similar image retrieval is presented in this paper. Utilizing database images previously annotated by keywords, the proposed method estimates unknown semantic features of a query image. First, our method performs semantic clustering of the database images and derives a new map from a nonlinear eigenspace of visual and semantic features in each cluster. This map accurately provides the semantic features for the images belonging to each cluster by using their visual features. Further, in order to select the optimal cluster including the query image, the proposed method monitors errors of the visual features caused by the semantic feature estimation process. Then, even if any semantics of the query image are unknown, its semantic features are successfully estimated by the optimal cluster. Experimental results verify the effectiveness of the proposed method for semantic image retrieval.