The traditional [ ]-means algorithm has been widely used as a simple and efficient clustering method. However, the performance of this algorithm is highly dependent on the selection of initial cluster centers. Therefore, the method adopted for choosing initial cluster centers is extremely important. In this letter, we redefine the density of points according to the number of its neighbors, as well as the distance between points and their neighbors. In addition, we define a new distance measure that considers both Euclidean distance and density. Based on that, we propose an algorithm for selecting initial cluster centers that can dynamically adjust the weighting parameter. Furthermore, we propose a new internal clustering validation measure, the clustering validation index based on the neighbors (CVN), which can be exploited to select the optimal result among multiple clustering results. Experimental results show that the proposed algorithm outperforms existing initialization methods on real-world data sets and demonstrates the adaptability of the proposed algorithm to data sets with various characteristics.

The minimum spanning tree- (MST-) based clustering method can identify clusters of arbitrary shape by removing inconsistent edges. The definition of the inconsistent edges is a major issue that has to be addressed in all MST-based clustering algorithms. In this paper, we propose a novel MST-based clustering algorithm through the cluster center initialization algorithm, called cciMST. First, in order to capture the intrinsic structure of the data sets, we propose the cluster center initialization algorithm based on geodesic distance and dual densities of the points. Second, we propose and demonstrate that the inconsistent edge is located on the shortest path between the cluster centers, so we can find the inconsistent edge with the length of the edges as well as the densities of their endpoints on the shortest path. Correspondingly, we obtain two groups of clustering results. Third, we propose a novel intercluster separation by computing the distance between the points at the intersection of clusters. Furthermore, we propose a new internal clustering validation measure to select the best clustering result. The experimental results on the synthetic data sets, real data sets, and image data sets demonstrate the good performance of the proposed MST-based method.

Few-shot semantic segmentation is a challenging task that aims to segment novel classes in the query images given only a few annotated support samples. Most existing prototype-based approaches extract global or local prototypes by global average pooling (GAP) or clustering to represent all object information. Subsequently, the prototype information is employed as guidance for query image segmentation. However, these frameworks fail to fully mine the object details and ignore information from query images. Consequently, we propose a Dual-Guided Frequency Prototype Network (DGFPNet) to solve these issues. Specifically, to mine the global and local object information, a Frequency Prototype Generation Module (FPGM) is first proposed to extract more comprehensive frequency prototypes by multi-frequency pooling (MFP) in the DCT domain. Then, with the guidance of support and query information, a Dual-Guided Selection Module (DGSM) is presented to produce the query attention mask and select more effective prototypes. Based on the query attention mask and support information, the generalized object information is integrated into the feature with the proposed Feature Generalization Module (FGM). Finally, we propose a Multi-Dimension Feature Enrichment Decoder Module (MDFEDM) to capture multi-dimension object information and tackle hard pixels for refining the final segmentation results. Extensive experiments on PASCAL-5 ⁱ and COCO-20 ⁱ show that our model achieves new state-of-the-art performances. Our code will be released at https://github.com/ChunLinWen/DGFPNet.