Attention shift-based multiple saliency object segmentation

Abstract

Object segmentation is an important but highly challenging problem in computer vision and image processing. An attention shift-based multiple saliency object segmentation model, called ASMSO, is introduced. The proposed ASMSO could produce a pool of potential object regions for each saliency object and be applicable to multiple saliency object segmentation. The potential object regions are produced by combing the methods of gPb-owt-ucm and min-cut graph, whereas the saliency objects are detected by a visual attention model with an attention shift mechanism. In order to deal with various scenes, the model attention shift-based multiple saliency object segmentation (ASMSO) contains different features which include not only traditional features, such as color, uniform, and texture, but also a new position feature originating from proximity of Gestalt theory. Experiments on the training set of PASCAL VOC2012 segmentation dataset not only show that traditional color feature and the proposed position feature work much better than features of texture and uniformity, but also prove that ASMSO is suitable for multiple object segmentation. In addition, experiments on a traditional saliency dataset show that ASMSO could also be applied to traditional saliency object segmentation and performs much better than the state-of-the-art method.