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Multi-view partial multi-label learning (MVPML) is a fundenmental problem where each sample is linked to multiple kinds of features and candidate labels, including ground-truth and noise labels. The key problem of MVPML is how to manipulate the multiple features and recover the ground-truth labels from candidate label set. To this end, this study designs a novel Graph-based Multi-view Partial Multi-label model named as GMPM, which combines the multi-view information detection, valuable label selection and multi-label predictor model learning into a unified optimization model. To be specific, GMPM first exploits the consensus information across multiple views by learning the view-specific similarity graph and fuses multiple graphs into a target one. Then, we divide the observed label set into two parts: the ground-truth part and the noise part, where the latter is associated with a sparse constraint to make sure the former is clean. Furthermore, we embed the learned unified similarity graph into the process of label disambiguation to restore a more reliable ground-truth label matrix. Finally, the resulting multi-label predictive model is learned with the help of ground-truth label matrix. Extensive experiments on six common used datasets demonstrate that the proposed GMPM achieves comparable performance over the state-of-the-arts.

Lane marks on roads are among the most important items of road scene information in the process of autonomous driving, and lane-mark extraction based on visual cognitive computing is one of the most important components of advanced driving assistance systems in intelligent transportation system. Onboard cameras mounted on the front of autonomous vehicles capture road scene images from which lane marks are extracted. This paper proposes a new lane-mark extraction algorithm with four major parts. First, this paper handles the road images captured from onboard cameras by grayscale and fast median filter. Then, we exploit the characteristics of lane marks in road images as constraints to propose lane-features filter based on multi-constraints used to extract lane marks. Then, a clustering algorithm based on the double point removal of a p-least squares algorithm is proposed to cluster features, and recursive dichotomy algorithm is used to fit the candidate lane marks. Finally, we carry out verification and optimization on candidate lane marks to obtain more accurate and stable extraction results. In our experiment, we divide the common complex road scenes into four categories. The results show that the proposed method can robustly extract lane marks under various complex real conditions. This paper also proposes forward a method to evaluate the results of lane-mark extraction, and partial test results are evaluated.