Feature selection using visual saliency for content-based image retrieval
Saliency algorithms in content-based image retrieval are employed to retrieve the most important regions of an image with the idea that these regions hold the essence of representative information. Such regions are then typically analysed and described for future retrieval/classification tasks rather than the entire image itself - thus minimising computational resources required. We show that we can select a small number of features for indexing using a visual saliency measure without reducing the performance of classifiers trained to find objects.