Bayesian optimization of the scale saliency filter
Image and Vision Computing
Multi-dimensional Scale Saliency Feature Extraction Based on Entropic Graphs
ISVC '08 Proceedings of the 4th International Symposium on Advances in Visual Computing, Part II
Co-segmentation of image pairs with quadratic global constraint in MRFs
ACCV'07 Proceedings of the 8th Asian conference on Computer vision - Volume Part II
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The aim of salient point detection is to find distinctive events in images. Salient features are generally determined from the local differential structure of images. They focus on the shape saliency of the local neighborhood. The majority of these detectors is luminance based which has the disadvantage that the distinctiveness of the local color information is completely ignored. To fully exploit the possibilities of color image salient point detection, color distinctiveness should be taken into account next to shape distinctiveness. In this paper color distinctiveness is explicitly incorporated into the design of saliency detection. The algorithm, called color saliency boosting, is based on an analysis of the statistics of color image derivatives. Isosalient color derivatives can be closely approximated by ellipsoidal surfaces in color derivative space. Based on this remarkable statistical finding, isosalient derivatives are transformed by color boosting to have equal impact on the saliency. Color saliency boosting is designed as a generic method easily adaptable to existing feature detectors. Results show that substantial improvements in information content are acquired by targeting color salient features. Further, the generality of the method is illustrated by applying color boosting to multiple existing saliency methods.