| It is revealed that the vision information plays about 83% role of the whole information which is received by human.Especially in the age of big data,it is worthwhile to do analysis of images and videos.Therefore,image segmentation is a hot topic and basic problem in the field of understanding and intelligentized processing of vision semantics.Moreover,image segmentation make a connection between the low level features and the high level semantics.Over decades,although an ocean of segmentation algorithms are proposed,there are still some universal problems,such as time complexity,segmentation accuracy and self-adaptive image semantics.In particular,the foreground segmentation is a research brach of image segmetation.Aimming at the efficiency and accuracy problems of foreground segmentation,in this thesis there are twofold researches as follows.Aimming for the problem that most of foreground segmentation algorithms are timeconsuming,and the trimaps used in the matting step are labelled manually.In this thesis,we propose a fast interactive foreground extraction method based on the superpixel GrabCut and image matting.Specifically,we frst extract superpixels from a given image and apply GrabCut on them to obtain a raw mask.Due to that the resulting mask border is hard and toothing,we further propose fast and adaptive trimaps(FATs),and construct a FATs-based Shared matting for computing a refined mask.Finally,by interactive processing,we can obtain thefinal foreground.Experimental results on the BSDS500 and alphamatting datasets demonstrate that our proposed method is superior to four representative methods whether in the vision view or in the three evaluation criteria,MSE,SAD and E-Time.Aimming at the existing problem of matting,in this thesis a manifold based matting framework named Patch Alignment Manifold Matting(PAMM)is firstly proposed.In particular,we first propose a part model of color space in the local image patch.Furthermore,we perform whole alignment optimization for approximating the alpha results by using subspace reconstructing error.Finally,we utilize an efficient Nesterov algorithm to solve the optimization problem.As an application of the framework,some new manifold learning matting algorithms,such as named ISOMAP Matting and its derived Boosting ISOMAP Matting(BIM),are also proposed.Alphamatting dataset is selected as the benchmark for the competitive matting algorithms in the experiment.The experimental results show the effectiveness of the manifold matting framework.In addition,the ISOMAP Matting and BIM can deal with the nonlinear data distribution and better preserve discriminability of pixel classes.Furthermore,the good performance of the ISOMAP Matting and BIM is demonstrated,both qualitatively and quantitatively. |
