Saliency detection for content-aware image resizing

Content aware image re-targeting methods aim to arbitrarily change image aspect ratios while preserving visually prominent features. To determine visual importance of pixels, existing re-targeting schemes mostly rely on grayscale intensity gradient maps. These maps show higher energy only at edges of objects, are sensitive to noise, and may result in deforming salient objects. In this paper, we present a computationally efficient, noise robust re-targeting scheme based on seam carving by using saliency maps that assign higher importance to visually prominent whole regions (and not just edges). This is achieved by computing global saliency of pixels using intensity as well as color features. Our saliency maps easily avoid artifacts that conventional seam carving generates and are more robust in the presence of noise. Also, unlike gradient maps, which may have to be recomputed several times during a seam carving based re-targeting operation, our saliency maps are computed only once independent of the number of seams added or removed.