Segment an image by looking into an image corpus
This paper investigates how to segment an image into semantic regions by harnessing an unlabeled image corpus. First, the image segmentation task is recast as a small-size patch grouping problem. Then, we discover two novel patch-pair priors, namely the first-order patch-pair density prior and the second-order patch-pair co-occurrence prior, founded on two statistical observations from the natural image corpus. The underlying rationalities are: 1) a patch-pair falling within the same object region generally has higher density than a patch-pair falling on different objects, and 2) two patch-pairs with high co-occurrence frequency are likely to bear similar semantic consistence confidences (SCCs), i.e. the confidence of the consisted two patches belonging to the same semantic concept. These two discriminative priors are further integrated into a unified objective function in order to augment the intrinsic patch-pair similarities, originally calculated using patch-level visual features, into the semantic consistence confidences. Nonnegative constraint is also imposed over the output variables and an efficient iterative procedure is provided to seek the optimal solution. The ultimate patch grouping is conducted by first building a similarity graph, which takes the atomic patches as vertices and the augmented patch-pair SCCs as edge weights, and then employing the popular Normalized Cut approach to group patches into semantic clusters. Extensive image segmentation experiments on two public databases clearly demonstrate the superiority of the proposed approach over various state-of-the-arts unsupervised image segmentation algorithms.