Salience computation in images based on perceptual distinctness

• A salience computation approach that uses perceptual distinctness is proposed.
• A novel representation of image pattern consistent with perceptual cues is used.
• The representation, a 4 element array of vector and matrix norms, is justified.
• Salience is computed by discriminating patterns around pixels from all in the image.
• Qualitative and quantitative results show the effectiveness of the proposed approach.

A salience computation approach based on perceptual distinctness is proposed in this paper that predicts human eye fixation locations in images. The approach uses a novel representation of pattern in a patch around a pixel such that discrimination between patterns is in compliance with distinctness between them in-terms of a few well-known perceptual cues. Image patches of different sizes (resolutions) are considered. The representations of the value at and pattern around a pixel in different features such as lightness, color and orientation are discriminated from all those in the rest of the image to compute salience at that pixel. The effectiveness of the proposed salience computation approach in generating salience maps closer to human eye fixation density maps is demonstrated by comparing it both qualitatively and quantitatively to other existing state-of-the-art salience computation approaches. Several synthetic images and psychological patterns, and real-life images from five well-known datasets are considered for this purpose.