We propose a novel Greedy Graph Cut (GGC) algorithm to address the graph partitioning problem. The algorithm begins by treating each data point as an individual cluster and iteratively merges cluster pairs that maximize the reduction in the global objective function until the desired number of clusters is achieved. We provide a theoretical proof of the monotonic convergence of the objective function values throughout this process. To improve computational efficiency, the algorithm restricts merging operations to adjacent clusters, resulting in a computational complexity that scales nearly linearly with the sample size. A significant advantage of our greedy approach is its deterministic nature, which ensures consistent results across multiple runs. This stands in contrast to many existing algorithms that are sensitive to random initialization effects. We demonstrate the effectiveness of the proposed algorithm by applying it to the Normalized Cut (N-Cut) problem, a well-studied variant of graph partitioning. Extensive experimental results show that GGC consistently outperforms the conventional two-stage optimization approach-which involves eigendecomposition followed by k-means clustering-in solving the N-Cut problem. Furthermore, comparative analyses reveal that GGC achieves superior performance compared to several state-of-the-art clustering algorithms.

Current scene parsers have effectively distilled abstract relationships among refined instances, while overlooking the discrepancies arising from variations in scene depth. Hence, their potential to imitate the intrinsic 3D perception ability of humans is constrained. In accordance with the principle of perspective, we advocate first grading the depth of the scenes into several slices, and then digging semantic correlations within a slice or between multiple slices. Two attention-based components, namely the Scene Depth Grading Module (SDGM) and the Edge-oriented Correlation Refining Module (EoCRM), comprise our framework, the Line-of-Sight Depth Network (LoSDN). SDGM grades scene into several slices by calculating depth attention tendencies based on parameters with explicit physical meanings, e.g., albedo, occlusion, specular embeddings. This process allocates numerous multi-scale instances to each scene slice based on their line-of-sight extension distance, establishing a solid groundwork for ordered association mining in EoCRM. Since the primary step in distinguishing distant faint targets is boundary delineation, EoCRM implements edge-wise saliency quantification and association digging. Quantitative and diagnostic experiments on Cityscapes, ADE20K, and PASCAL Context datasets reveal the competitiveness of LoSDN and the individual contribution of each highlight. Visualizations display that our strategy offers clear benefits in detecting distant, faint targets.

Pattern recognition from two-dimensional (2D) images is an indispensable tache in computer vision. However, one legacy hinders its progress: multifarious visual distortions (e.g., partially occluded signs, fisheye respective, affine or 3D projections) caused by spatiotemporal-varying perturbations (e.g., shrunk, sharpening, overexposure, jitter, and motion) significantly degrade the performance of neural networks in terms of high-level intelligent behaviors (e.g., target localization and recognition). Leveraging the growing availability of capsule network (CapsNet), we suppress the deformation effect to the final prediction by proposing a CapsNetORB framework to implement distorted target recognition. Two highlights, the customized Siamese CapsNet (S-CapsNet) and vector-based oriented fast and rotated brief (VB-ORB), can cast a mutual positive stimulation: the former encodes capsule feature vectors for the later, whilst the later detects space-scale invariant interval dimensions (instead of pixels) to bridge association between source standard images (high-quality training images) and distorted ones (testing images). Thus, the category of one source standard image owning the most correspondences is the final predicted category. We believe that capsule vectors own higher representability and stability compared with conventional pixels/feature maps, which can be well exploited in feature learning while resisting visual distortions. Experimentally, we show that employing our pipeline for distorted target categorization can outperform state-of-the-arts by delivering promising performance on CUB-200-2011, Stanford Dogs, Stanford Cars, and our hand-crafted data set.