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Existing interactive image segmentation methods heavily rely on manual input, i.e. a sufficient quantity and correct locations of labels. In this paper, we propose a new interactive segmentation algorithm which aims to reduce human intervention and to generate high-quality segmentation results. In contrast to most energy minimizing based segmentation methods, the segmentation is cast as multi-classification in our proposed method. First, the input image is segmented into superpixels by using different methods. Then we build a dictionary consisting of all obtained superpixels and reconstruct samples represented by certain labeled superpixels. Finally, we learn a discriminative projection matrix through Fishers linear discriminant analysis (FLDA) algorithm, which learns a discriminative subspace for classification. The unlabeled superpixels are grouped into foreground or background, via calculating their minimal norm. Our method can capture long range grouping cues and reduce the sensitivity with respect to input label quantity and location of labels, by the combination of superpixels and discriminative dictionary. Extensive experiments are conducted both on MSRC and another challenging database in order to demonstrate the effectiveness of the proposed method. Quantitative and qualitative results show that our method is competitive to the state-of-the-art performance. [ABSTRACT FROM AUTHOR]

Highlights • A method for visual object tracking is proposed. • We propose a decomposition model in visual tracking. • The decomposition model is utilized in an integrated generative model and discriminative model tracking framework. • Alternating direction method of multipliers algorithm guarantees the convergence of the decomposition model. This paper presents a multi-task based object tracking algorithm via a collaborative model. Under the framework of particle filtering, we develop a multi-task sparse learning based generative and discriminative classifier model. In the generative model, we propose a histogram based subspace learning method which takes advantage of adaptive templates update. In the discriminative model, we introduce an effective method to compute the confidence value which assigns more weights to the foreground than the background. A decomposition model is employed to take the common features and outliers of each particle into consideration. The alternating direction method of multipliers (ADMM) algorithm guarantees the optimization problem can be solved robustly and accurately. Qualitative and quantitative comparisons with nine state-of-the-art methods demonstrate the effectiveness and efficiency of our method in handling various challenges during tracking. [ABSTRACT FROM AUTHOR]