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Image saliency detection using region contrast is often based on the premise that salient region has a contrast with the background which becomes a limiting factor if the color of the salient object background is similar. To overcome this problem associated with single image analysis, we propose to collect background regions from a collection of images where generative property of, say, natural images ensures that all the images are spun out of it hence negating any bias. Background regions are differentiated based on their geometric context where we use the ground and sky context as background. Finally, the aggregated map is generated using color contrast between the superpixels segments of the image and collection of background

Saliency Detection using Geometric Context Contrast Inferred from Natural Images