Saliency Modeling from Image Histograms

Abstract

We proposed a computational visual saliency modeling technique. The proposed technique makes use of a color co-occurrence histogram (CCH) that captures not only “how many” but also “where and how” image pixels are composed into a visually perceivable image. Hence the CCH encodes image saliency information that is usually perceived as the discontinuity between an image region or object and its surrounding. The proposed technique has a number of distinctive characteristics: It is fast, discriminative, tolerant to image scale variation, and involves minimal parameter tuning. Experiments over benchmarking datasets show that it predicts fixational eye tracking points accurately and a superior AUC of 71.25 is obtained.