Concurrent segmentation of categorized objects from an image collection

We propose a method for automatic segmentation of categorized objects from a collection of images in the same category, which employs a single auto-context model learned from all images without the need of using pixel level labels. Instead of extracting the salient objects from each image one by one, we extract the objects from all images simultaneously. The segmentation of the salient objects is iteratively performed, where the auto-context model is incrementally learned based on new segmentations of all images at each iteration. Upon convergence, we obtain not only the clean segmentations of the salient objects, but also an auto-context classifier learned on all images which can readily be exploited to segment categorized object from a new image. Our experiments validated the efficacy of our proposed approach.