Density-based clustering algorithms are widely used for their ability to handle complex datasets; however, their performance often depends on the definition of density and is sensitive to data shapes, density variations, and noise. To address these challenges, we propose a novel parameter-free clustering algorithm named "Bombing", which can automatically determine the number of clusters. The algorithm enhances adaptability to complex data structures by combining global and local density estimations and utilizing dynamically adjusted density measures. By introducing the concept of generalized clusters and employing a "bombing" process that propagates clustering outward from core points, the method effectively mitigates the impact of cluster shapes, overlaps, and noise. Additionally, through the analysis of adjacency degrees, the algorithm can automatically detect the optimal number of clusters without prior knowledge. Experimental results on various synthetic and real datasets show that our proposed algorithm outperforms existing methods in clustering accuracy and robustness. It effectively handles noise and cluster overlap, and excels in identifying the optimal value of K .

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-5iand COCO-20ishow that our model achieves new state-of-the-art performances. Our code will be released athttps://github.com/ChunLinWen/DGFPNet.

The bag of visual words (BOW) model has been widely applied in the field of image recognition and image classification. However, all scale‐invariant feature transform (SIFT) features are clustered to construct the visual words which result in a substantial loss of discriminative power for the visual words. The corresponding visual phrases will further render the generated BOW histogram sparse. In this study, the authors aim to improve the classification accuracy by extracting high discriminative SIFT features and feature pairs. First, high discriminative SIFT features are extracted with the within‐ and between‐class correlation coefficients. Second, the high discriminative SIFT feature pairs are selected by using minimum spanning tree and its total cost. Next, high discriminative SIFT features and feature pairs are exploited to construct the visual word dictionary and visual phrase dictionary, respectively, which are concatenated to a joint histogram with different weights. Compared with the state‐of‐the‐art BOW‐based methods, the experimental results on Caltech 101 dataset show that the proposed method has higher classification accuracy.

The traditional [Formula: see text]-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.