Detection of saliency map as image feature outliers using random projections based method

Abstract

We describe a novel method based on Random Projections for construction of image saliency maps. The method identifies outliers in the 2D projections of image point features as salient image points using Random Projections and kernel density estimation. We compare the method with other known methods in the area and validated on a number of benchmark images. The robustness of the method when Gaussian blurring is applied to an image is demonstrated and evaluated using F-statistics of several image quality metrics. Application of the proposed method for image processing is discussed.