SI-Cut: Structural Inconsistency Analysis for Image Foreground Extraction

Abstract

This paper presents a novel approach for extracting foreground objects from an image. Existing methods involve separating the foreground and background mainly according to their color distributions and neighbor similarities. This paper proposes using a more discriminative strategy, structural inconsistency analysis, in which the localities of color and texture are considered. Given an indicated rectangle, the proposed system iteratively maximizes the consensus regions between the original image and predicted structures from the known background. The object contour can then be extracted according to inconsistency in the predicted background and foreground structures. The proposed method includes an efficient image completion technique for structural prediction. The results of experiments showed that the extraction accuracy of the proposed method is higher than that of related methods for structural scenes, and is also comparable to that of related methods for less structural situations.