Radon transform bispectra and principal component analysis for RTS invariant image retrieval

摘要

Abstract: An image retrieval method is presented based on shape similarity measure for multimedia and imaging database system. In the proposed algorithm, the spatial and spectral properties of images are combined using the Radon transform, bispectra, and principal components analysis. For each model image in the database, the original 2D image data are reduced to a set of 1D projections via the Radon transform, and then a feature vector is calculated from the bispectra of the resultant 1D functions. The principal component analysis is applied to further reduce the dimension of the feature vector so that it can be stored along with the original image in the database at a small cost of memory. The derived feature vector is considered as the index or key of the corresponding image, which uniquely identifies the image independent of rotation, translation, and scaling. For image retrieval, the data feature vector is computed for a query image, and matched against the feature vectors of all the model images in the database using the Tanimoto similarity measure. The closely matching images are brought out as the searching results. The proposed technique has been tested on a large image database. The experimental results show that the retrieval accuracy is very high even for query images with low signal-to-noise ratio. !10