Content-based image retrieval via subspace-projected salient features

In this paper we present a novel image representation method which treats images as frequency histograms of salient features. The histograms are computed making use of linear discriminant analysis (LDA). The method em-ploys saliency feature extraction and image binarisation. Then subspace-projected features are extracted. Using the saliency maps as the positive and negative labels, the im-age features are mapped onto a lower-dimensional space using LDA. This enables us to construct a 3D-histogram by direct binning on the feature space. This gives rise to a ”cube ” of image features which have been projected onto a lower-dimensional space so as to maximise the separability of the salient regions with respect to the background. Im-age retrieval can be performed by computing the distances between the histograms for the query image and the images in the database. We demonstrate our algorithm on a real-world database and compare our results to those yielded by codebook representation. 1