Ensemble clustering has been a popular research topic in data mining and machine learning. Despite its significant progress in recent years, there are still two challenging issues in the current ensemble clustering research. First, most of the existing algorithms tend to investigate the ensemble information at the object-level, yet often lack the ability to explore the rich information at higher levels of granularity. Second, they mostly focus on the direct connections (e.g., direct intersection or pairwise co-occurrence) in the multiple base clusterings, but generally neglect the multi-scale indirect relationship hidden in them. To address these two issues, this paper presents a novel ensemble clustering approach based on fast propagation of cluster-wise similarities via random walks. We first construct a cluster similarity graph with the base clusters treated as graph nodes and the cluster-wise Jaccard coefficient exploited to compute the initial edge weights. Upon the constructed graph, a transition probability matrix is defined, based on which the random walk process is conducted to propagate the graph structural information. Specifically, by investigating the propagating trajectories starting from different nodes, a new cluster-wise similarity matrix can be derived by considering the trajectory relationship. Then, the newly obtained cluster-wise similarity matrix is mapped from the cluster-level to the object-level to achieve an enhanced co-association (ECA) matrix, which is able to simultaneously capture the object-wise co-occurrence relationship as well as the multi-scale cluster-wise relationship in ensembles. Finally, two novel consensus functions are proposed to obtain the consensus clustering result. Extensive experiments on a variety of real-world datasets have demonstrated the effectiveness and efficiency of our approach.

Litchi is often harvested by clamping and cutting the branches, which are small and can easily be damaged by the picking robot. Therefore, the detection of litchi branches is particularly significant. In this paper, an fully convolutional neural network-based semantic segmentation algorithm is proposed to semantically segment the litchi branches. First, the DeepLabV3+ semantic segmentation model is combined with the Xception depth separable convolution feature. Second, transfer learning and data enhancement are used to accelerate the convergence and improve the robustness of the model. Third, a coding and a decoding structure are adopted to reduce the number of network parameters. The decoding structure uses upsampling and the shallow features to fuse, and the same weight is assigned to ensure that the shallow feature semantics and the deep feature semantics are evenly distributed. Fourth, using atrous spatial pyramid pooling, we can better extract the semantic pixel position information without increasing the number of weight parameters. Finally, different sizes of hole convolution are used to ensure the prediction accuracy of small targets. Experiment results demonstrated that the DeepLabV3+ model using the Xception_65 feature extraction network obtained the best results, achieving a mean intersection over union (MIoU) of 0.765, which is 0.144 higher than the MIoU of 0.621 of the original DeepLabV3+ model. Meanwhile, the DeepLabV3+ model using the Xception_65 network has greater robustness, far exceeding the PSPNet_101 and ICNet in detection accuracy. The aforementioned results indicated that the proposed model produced better detection results. It can provide powerful technical support for the gripper picking robot to find fruit branches and provide a new solution for the problem of aim detection and recognition in agricultural automation.

No abstract

No abstract

No abstract