Salient instance segmentation refers to segmenting noticeable instance objects in images. In the face of multi-scale salient instances and overlapping instances, the existing salient instance segmentation methods have great limitations including inaccurate detection of large-scale instances, missing detection of small-scale instances, and wrong segmentation of overlapping instances. In order to solve these problems, a new multi-scale salient instance segmentation network (MSISNet) based on encoder-decoder is proposed. Firstly, a receptive field encoder (RFE) is designed to alleviate the problems of inaccurate detection of large-scale instances, missing detection of small-scale instances, and especially wrong segmentation of overlapping instances. Then, a pyramid decoder (PD) for the detection branch is designed to further alleviate the problem of inaccurate detection of large-scale instances and the difficulty in locating small-scale instances. Finally, a multi-stage decoder (MSD) is designed to improve the quality of the segmentation mask. Experiments on salient instance segmentation dataset Salient Instance Segmentation-1K (SIS-1K) have been conducted and the results show that the proposed method MSISNet is superior to the existing salient instance segmentation methods MSRNet and S4Net, and achieves better segmentation accuracy and speed.

Facing a large number of unlabeled data and a small number of labeled data, semisupervised sparse feature selection has received increasing attention in recent years. However, most semisupervised feature selection algorithms are developed for single-view data and cannot naturally handle multiview data. Moreover, most existing semisupervised sparse feature selection methods are based on Laplacian regularization, which is a lack of extrapolating power. To overcome the above-mentioned drawbacks, we present a multiview Hessian semi-supervised sparse feature selection (MHSFS) framework in this paper. MHSFS can directly accomplish multiview sparse feature selection by exploiting multiview learning to reveal and leverage the correlated and complemental information among different views. In addition, MHSFS can achieve better performance based on Hessian regularization, which favors functions whose values linearly vary with respect to geodesic distance and preserves the local manifold structure well. A simple yet efficient iterative method is proposed to solve the objective function, followed by convergence analysis. We apply the proposed method into different multimedia analysis tasks, such as image annotation, video concept detection, and 3D motion analysis. The results show that MHSFS outperforms the state-of-the-art sparse feature selection methods and achieves good performance.

Semi-supervised sparse feature selection, which can exploit the small number labeled data and large number unlabeled data simultaneously, has become an important technique in many applications on large-scale web image owing to its high efficiency and effectiveness. Recently, graph Laplacian-based semi-supervised sparse feature selection has obtained considerable attention, but it suffers with only few labeled data because Laplacian regularization is short of extrapolating power. In this paper we propose a novel semi-supervised sparse feature selection framework based on Hessian regularization and l2,1/2- matrix norm, namely Hessian sparse feature selection based on L2,1/2- matrix norm (HFSL). Hessian regularization favors functions whose values vary linearly with respect to geodesic distance and preserves the local manifold structure well, leading to good extrapolating power to boost semi-supervised learning, and then to enhance HFSL performance. The l2,1/2-matrix norm model makes HFSL select the most discriminative sparse features with good robustness. An efficient iterative algorithm is designed to optimize the objective function. We apply our algorithm into the image annotation task and conduct extensive experiments on two web image datasets. The results demonstrate that our algorithm outperforms state-of-the-art sparse feature selection methods and is promising for large-scale web image applications.