For real-world applications, multilabel learning usually suffers from unsatisfactory training data. Typically, features may be corrupted or class labels may be noisy or both. Ignoring noise in the learning process tends to result in an unreasonable model and, thus, inaccurate prediction. Most existing methods only consider either feature noise or label noise in multilabel learning. In this paper, we propose a unified robust multilabel learning framework for data with hybrid noise, that is, both feature noise and label noise. The proposed method, hybrid noise-oriented multilabel learning (HNOML), is simple but rather robust for noisy data. HNOML simultaneously addresses feature and label noise by bi-sparsity regularization bridged with label enrichment. Specifically, the label enrichment matrix explores the underlying correlation among different classes which improves the noisy labeling. Bridged with the enriching label matrix, the structured sparsity is imposed to jointly handle the corrupted features and noisy labeling. We utilize the alternating direction method (ADM) to efficiently solve our problem. Experimental results on several benchmark datasets demonstrate the advantages of our method over the state-of-the-art ones.

Data-driven saliency detection has attracted strong interest as a result of applying convolutional neural networks to the detection of eye fixations. Although a number of image-based salient object and fixation detection models have been proposed, video fixation detection still requires more exploration. Different from image analysis, motion and temporal information is a crucial factor affecting human attention when viewing video sequences. Although existing models based on local contrast and low-level features have been extensively researched, they failed to simultaneously consider interframe motion and temporal information across neighboring video frames, leading to unsatisfactory performance when handling complex scenes. To this end, we propose a novel and efficient video eye fixation detection model to improve the saliency detection performance. By simulating the memory mechanism and visual attention mechanism of human beings when watching a video, we propose a step-gained fully convolutional network by combining the memory information on the time axis with the motion information on the space axis while storing the saliency information of the current frame. The model is obtained through hierarchical training, which ensures the accuracy of the detection. Extensive experiments in comparison with 11 state-of-the-art methods are carried out, and the results show that our proposed model outperforms all 11 methods across a number of publicly available datasets.

Given a group photograph, it is interesting and useful to judge whether the characters in it share specific kinship relation, such as father-daughter, father-son, mother-daughter, or mother-son. Recently, facial image-based kinship verification has attracted wide attention in computer vision. Some metric learning algorithms have been developed for improving kinship verification. However, most of the existing algorithms ignore fusing multiple feature representations and utilizing kernel techniques. In this paper, we develop a novel weighted graph embedding-based metric learning (WGEML) framework for kinship verification. Inspired by the fact that family members usually show high similarity in facial features like eyes, noses, and mouths, despite their diversity, we jointly learn multiple metrics by constructing an intrinsic graph and two penalty graphs to characterize the intraclass compactness and interclass separability for each feature representation, respectively, so that both the consistency and complementarity among multiple features can be fully exploited. Meanwhile, combination weights are determined through a weighted graph embedding framework. Furthermore, we present a kernelized version of WGEML to tackle nonlinear problems. Experimental results demonstrate both the effectiveness and efficiency of our proposed methods.

In conventional wisdom of video modeling, the background is often treated as the primary target and foreground is derived using the technique of background subtraction. Based on the observation that foreground and background are two sides of the same coin, we propose to treat them as peer unknown variables and formulate a joint estimation problem, called Hierarchical modeling and Alternating Optimization (HMAO). The motivation behind our hierarchical extensions of background and foreground models is to better incorporate a priori knowledge about the disparity between background and foreground. For background, we decompose it into temporally low-frequency and high-frequency components for the purpose of better characterizing the class of video with dynamic background; for foreground, we construct a Markov random field prior at a spatially low resolution as the pivot to facilitate the noise-resilient refinement at higher resolutions. Built on hierarchical extensions of both models, we show how to successively refine their joint estimates under a unified framework known as alternating direction multipliers method. Experimental results have shown that our approach produces more discriminative background and demonstrates better robustness to noise than other competing methods. When compared against current state-of-the-art techniques, HMAO achieves at least comparable and often superior performance in terms of F-measure scores, especially for video containing dynamic and complex background.

Wind power curve modeling is a challenging task due to the existence of inconsistent data, in which the recorded wind power is far away from the theoretical wind power at a given wind speed. In this case, confronted with these samples, the estimated errors of wind power will become large. Thus, the estimated errors will present two properties: heteroscedasticity and error distribution with a long tail. In this paper, according to the above-mentioned error characteristics, the heteroscedastic spline regression model (HSRM) and robust spline regression model (RSRM) are proposed to obtain more accurate power curves even in the presence of the inconsistent samples. The results of power curve modeling on the real-world data show the effectiveness of HSRM and RSRM in different seasons. As HSRM and RSRM are optimized by variational Bayesian, except the deterministic power curves, probabilistic power curves, which can be used to detect the inconsistent samples, can also be obtained. Additionally, with the data processed by replacing the wind power in the detected inconsistent samples with the wind power on the estimated power curve, the forecasting results show that more accurate wind power forecasts can be obtained using the above-mentioned data processing method.