Incomplete multi-view clustering has gained considerable attention in recent years due to the prevalence of incomplete multi-view data in real-world applications. However, existing methods often struggle to effectively deal with large-scale datasets, particularly those with a significant number of missing instances. To address these issues, we propose a novel method called Hubness-Enabled Clustering and Recovery for Large-Scale Incomplete Multi-View Data (HENRI). HENRI utilizes the consensus hubs of all views to identify informative anchors to handle large-scale incomplete datasets. Furthermore, it incorporates a novel sample-level fusion strategy that effectively integrates information from all views, leading to remarkable outcomes in both cluster formation and missing data reconstruction. HENRI demonstrates exceptional capability in capturing the underlying structures of the data and recovering missing information, even when faced with a significant number of instances with incomplete data in partial views. To validate its effectiveness, we conducted experiments on 6 complete datasets and 31 incomplete datasets, comparing against 11 baseline methods. The results are impressive, demonstrating the superior performance of HENRI over the state-of-the-art methods.

Time series research in academic and industrial fields has attracted wide attention. However, the frequency information contained in time series still lacks effective modeling. The studies found that time series forecasting relies on different frequency patterns: short-term series forecasting relies more on high-frequency components, while long-term forecasting focuses more on low-frequency data. To better describe the multifrequency mode, a dual-stage multifeature adaptive frequency domain prediction model (DMAFD) is proposed in this paper. DMAFD contains two stages. First, it adopts the XGBoost algorithm to obtain a feature vector by analyzing the feature importance. Second, the frequency feature extraction of time series and the frequency aware modeling of the target sequence is integrated, for building an end-to-end prediction network based on the dependence of time series on frequency mode. The innovation is reflected in the fact that the prediction network can automatically focus on multifrequency components according to the dynamic evolution of the input sequence. Extensive experiments on four real data sets from different fields show that DMAFD obtains higher accuracy and smaller lags in time step analysis compared with state-of-the-art algorithms.

Multiview spectral clustering aims to separate data into different clusters efficiently by the use of multiview information. Many studies learn the affinity matrix from the original high-dimensional data, whose noise goes against the clustering results. Besides, some methods based on self-representation subspace clustering have a high time complexity. In this paper, we propose a simple, yet effective, and efficient method named Kernel-based Low-rank Tensorized Multiview Spectral Clustering (KLTMSC) to address these issues. Instead of using the original data to get the affinity matrix, KLTMSC learns the affinity matrix from kernel representation of the high-dimensional data to reduce the noisy information. Furthermore, to be robust to noise, the low-rank tensor is learned in the process of exploring the high-order correlations between data. Experiments on real-world data sets show that our method not only yields better results but also is quite time-saving compared with other state-of-the-art models.

As we all know, video frame rate determines the quality of the video. The higher the frame rate, the smoother the movements in the picture, the clearer the information expressed, and the better the viewing experience for people. Video interpolation aims to increase the video frame rate by generating a new frame image using the relevant information between two consecutive frames, which is essential in the field of computer vision. The traditional motion compensation interpolation method will cause holes and overlaps in the reconstructed frame, and is easily affected by the quality of optical flow. Therefore, this paper proposes a video frame interpolation method via optical flow estimation with image inpainting. First, the optical flow between the input frames is estimated via combined local and global-total variation (CLG-TV) optical flow estimation model. Then, the intermediate frames are synthesized under the guidance of the optical flow. Finally, the nonlocal self-similarity between the video frames is used to solve the optimization problem, to fix the pixel loss area in the interpolated frame. Quantitative and qualitative experimental results show that this method can effectively improve the quality of optical flow estimation, generate realistic and smooth video frames, and effectively increase the video frame rate.

Automatic and accurate semantic segmentation of pathological structures in medical images is challenging because of noisy disturbance, deformable shapes of pathology, and low contrast between soft tissues. Classical superpixel-based classification algorithms suffer from edge leakage due to complexity and heterogeneity inherent in medical images. Therefore, we propose a deep U-Net with superpixel region merging processing incorporated for edge enhancement to facilitate and optimize segmentation. Our approach combines three innovations: (1) different from deep learning--based image segmentation, the segmentation evolved from superpixel region merging via U-Net training getting rich semantic information, in addition to gray similarity; (2) a bilateral filtering module was adopted at the beginning of the network to eliminate external noise and enhance soft tissue contrast at edges of pathogy; and (3) a normalization layer was inserted after the convolutional layer at each feature scale, to prevent overfitting and increase the sensitivity to model parameters. This model was validated on lung CT, brain MR, and coronary CT datasets, respectively. Different superpixel methods and cross validation show the effectiveness of this architecture. The hyperparameter settings were empirically explored to achieve a good trade-off between the performance and efficiency, where a four-layer network achieves the best result in precision, recall, F-measure, and running speed. It was demonstrated that our method outperformed state-of-the-art networks, including FCN-16s, SegNet, PSPNet, DeepLabv3, and traditional U-Net, both quantitatively and qualitatively. Source code for the complete method is available at https://github.com/Leahnawho/Superpixel-network.