Detection of visual attention regions in images using robust subspace analysis

In this paper, we describe a new framework to extract visual attention regions in images using robust subspace estimation and analysis techniques. We use simple features like hue and intensity endowed with scale adaptivity in order to represent smooth and textured areas in an image. A polar transformation maps homogeneity in the features into a linear subspace that also encodes spatial information of a region. A new subspace estimation algorithm based on the Generalized Principal Component Analysis (GPCA) is proposed to estimate multiple linear subspaces. Sensitivity to outliers is achieved by weighted least squares estimate of the subspaces in which weights calculated from the distribution of K nearest neighbors are assigned to data points. Iterative refinement of the weights is proposed to handle the issue of estimation bias when the number of data points in each subspace is very different. A new region attention measure is defined to calculate the visual attention of each region by considering both feature contrast and spatial geometric properties of the regions. Compared with existing visual attention detection methods, the proposed method directly measures global visual attention at the region level as opposed to pixel level.