Abstract
We develop an image similarity descriptor for an image pair, based on deep features. The development consists of two parts - selecting the deep layer whose features are to be included in the descriptor, and a representation of the similarity between the images in the pair. The selection of the deep layer follows a sparse representation of the feature maps followed by multi-output support vector regression. The similarity representation is based on a novel correlation between the histograms of the feature maps of the two images. Experiments to demonstrate the effectiveness of the proposed descriptor are carried out on four applications that can be cast as classification tasks.
Published Version
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