Detecting antagonistic near-balanced dense subgraphs is a crucial problem for community search and conflict detection in graph and network analysis, which has wide applications in social media analysis, business, geopolitics etc. To tackle the absence of a unified definition of antagonistic situation, we propose theantagonismmeasure, which quantifies the quality of subgraphs by three dimensions: polarity, internal cohesion, and external antagonistic normalized density. Inspired by the contribution of small antagonistic balanced patterns to antagonism and balance, this paper introduces an efficient algorithmic framework to mine locally specific pattern densest subgraph structure to find subgraphs with high antagonism. We in particular jointly consider$hx$-pattern compact number and L$hx$PDS and design a new Iterative Propose-Prune-and-Verify pipeline in signed graphs (IPPV-s) for top-$k$L$hx$PDS detection. The key contributions are: (1) The antagonism measure is defined, bridging the gap between structural density and balance theory. (2) An efficient algorithmic pipeline that combines convex optimization with maximum flow verification is proposed, which enables scalable and efficient antagonistic near-balanced dense subgraph discovery. (3) Extensive experiments on real signed network datasets show the effectiveness of our approach in uncovering meaningful subgraphs that capture both cooperative and conflicting dynamics.

The Budget Limited Coreness Maximization (BLCM) problem aims to enhance average user engagement by activating a limited number of connections, i.e., inserting up to b edges to maximize the coreness gain of all vertices in a graph. Due to the cascading feature, we prove the BLCM is NP-hard, APX-hard, and not submodular, meaning greedy sequential edge insertion fails to deliver satisfactory results. As a result, solving BLCM requires combinatorial edge insertion and must face the combinatorial exploration difficulty. This paper proposes the first effective and polynomial-time approach to BLCM. It embeds local combinatorial optimization into global greedy search to boost the benefits of combinatorial optimization while restricting its complexity. Specifically, we propose efficient methods to evaluate the cascaded coreness improvements of two local combinatorial strategies, i.e., when a leader or a group of nodes increase their coreness values via local edge insertion. Note that the key difficulty lies in evaluating the cascading effects. Based on these, we propose three efficient combinatorial edge insertion strategies: (1) Leader-Centric Greedy Insertion (LCGI), (2) Group-Centric Greedy Insertion (GCGI), and (3) a Leader-Group Balance (LGB) insertion. LCGI greedily finds the most influential leader that can produce the highest coreness gain together with its followers. GCGI finds the most influential group that can promote the most coreness gain. LGB combines the two strategies to select edge combinations adaptively. We prove the low complexity of LCGI, GCGI and LGB. Experiments conducted on 13 real-world datasets highlight their practical utility and superiority over existing approaches.

Detecting locally, non-overlapping, near-clique densest subgraphs is a crucial problem for community search in social networks. As a vertex may be involved in multiple overlapped local cliques, detecting locally densest sub-structures considering h -clique density, i.e., locally h-clique densest subgraph (LhCDS) attracts great interests. This paper investigates the L h CDS detection problem and proposes an efficient and exact algorithm to list the top- k non-overlapping, locally h -clique dense, and compact subgraphs. We in particular jointly consider h -clique compact number and L h CDS and design a new ''Iterative Propose-Prune-and-Verify'' pipeline (IPPV) for top- k L h CDS detection. (1) In the proposal part, we derive initial bounds for h -clique compact numbers; prove the validity, and extend a convex programming method to tighten the bounds for proposing L h CDS candidates without missing any. (2) Then a tentative graph decomposition method is proposed to solve the challenging case where a clique spans multiple subgraphs in graph decomposition. (3) To deal with the verification difficulty, both a basic and a fast verification method are proposed, where the fast method constructs a smaller-scale flow network to improve efficiency while preserving the verification correctness. The verified L h CDSes are returned, while the candidates that remained unsure reenter the IPPV pipeline. (4) We further extend the proposed methods to locally more general pattern densest subgraph detection problems. We prove the exactness and low complexity of the proposed algorithm. Extensive experiments on real datasets show the effectiveness and high efficiency of IPPV. Codes are available at: https://github.com/Elssky/IPPV

With the development of computer vision, the semantic segmentation of remote sensing images, which has become an important topic, has been utilized in various applications for image content analysis and understanding, such as urban planning, natural disaster monitoring, and land resource management. Many approaches have been proposed to address these problems. However, due to obvious differences in resolution, spatial structure, and semantics between remote sensing images and ordinary images, the semantic segmentation of remote sensing images is still challenging. In this paper, we propose a novel multiscale image generation network (MIGN) that can efficiently generate high-resolution segmentation results by considering both details and boundary information. In particular, a multi-attention mechanism method for semantic segmentation of remote sensing images is designed. The attention weight is calculated by capturing the interaction of cross dimensions in a two-branch structure, which can learn the underlying feature information and guarantee the performance of each pixel feature for final classification. We also propose an edge supervised module to ensure that the segmentation boundary has a more accurate performance. A multiscale image fusion algorithm based on the Bayes model is proposed to improve the accuracy of the segmentation module. The performance of our model is evaluated on the ISPRS Vaihingen and Potsdam datasets. The results show that our method is superior to the most advanced image segmentation methods in terms of MIoU and pixel accuracy.

Semantic segmentation of remote sensing (RS) images plays a vital role in a variety of fields, including urban planning, natural disaster monitoring, and land resource management. Due to the complexity and low resolution of RS images, many approaches have been proposed to handle the related task. However, these previously developed approaches dedicate to contextual interaction but ignore the cross-scale semantic correlation and multi-scale boundary information. Therefore, we propose a Cross-scale Graph Interaction Network (CGIN) to address semantic segmentation problems of RS images, which consists of a semantic branch and a boundary branch. In the semantic branch, we first apply atrous convolution to extract multi-scale semantic features of RS images. Particularly, based on the multi-scale semantic features, a Cross-scale Graph Interaction (CGI) module is introduced, which establishes cross-scale graph structures and performs adaptive graph reasoning to capture the cross-scale semantic correlation of RS objects. In the boundary branch, we propose a Multi-scale Boundary Feature Extraction (MBFE) module that utilizes atrous convolutions with different dilation rates to extract multi-scale boundary features. Finally, to address the problem of sparse boundary pixels in the fusion process of the two branches, we propose a Multi-scale Similarity-guided Aggregation (MSA) module by calculating the similarity of semantic features and boundary features at the corresponding scale, which can emphasize the boundary information in semantic features. Our proposed CGIN outperforms state-of-the-art approaches in numerical experiments conducted on two benchmark remote sensing datasets.

As we all know, remote sensing (RS) images contain multi-scale and numerous RS objects, along with massive and complex spatial topological relationships, such as the adjacency, proximity relations of same-scale objects, and inclusion relations of cross-scale objects. However, the existing semantic segmentation methods have never explored the cross-scale relations, which are especially important when comes to the situation that the RS objects cannot be accurately identified, they could be supplemented by the surrounding contents. To address the above concern, we propose a scale-relation joint decoupling network (SRJDN) for the semantic segmentation of RS images by simultaneously considering decoupling scales and decoupling relations to excavate more complete relationships of multi-scale RS objects. The SRJDN is performed by following three steps, namely scale decoupling (SD), relation decoupling (RD), and fine-granularity guided fusion (FGF). The SD module uses dilated convolution with different rates to decouple RS objects into different scale feature groups, from small to large scales. Afterward, the RD considers all the spatial topological relationships and decouples these relationships according to the scale, which is divided into two parts, including same-scale relation extraction (SSRE) and cross-scale relation extraction (CSRE). The SSRE establishes the graph structures at each scale independently to mine the relationships of same-scale RS objects and the CSRE constructs the graph in a unified pattern between cross-scales to explore cross-scale target relationships. Third, the FGF module regards small-scale features as fine-granularity representation and applies its attention map to guide the learning of other scale features, which could mine more reliable and comprehensive saliency information and improve the feature consistency. Numerical experiments conducted on two large-scale fine-resolution RS image datasets empirically demonstrate the robustness of the proposed joint decoupling strategy and the effectiveness of the fine-granularity guided fusion in RS image semantic segmentation tasks.

This paper considers the localization problem of sensors in mobile wireless sensor networks. It proposes a combined localization technique, using both fingerprinting and accelerometer information. The proposed approach consists of two phases. In the first one, a power map is constructed over the surveillance area. In the second phase, nodes are localized and a first position estimate is computed using the constructed power map. A second estimate is also given using accelerometer information. Both estimates are then combined using interval analysis, solutions being boxes including the real positions of the nodes. Simulation results show that the combined method improves the positioning performance, compared to methods based only on fingerprinting or accelerometer information.

This paper proposes a method for background modeling and foreground detection in video. This method divides the background into two layers, the dynamic layer and the static layer. An energy descriptor is proposed to analysis the motion state in dynamic layer while a grid filter is proposed to reduce the negative impact of sudden illumination change such as light switching off. Experiment results compared with four typical algorithms show that this method outperforms others in most challenging videos including sudden illumination change and some complex backgrounds.

Human matching between different fields of view is a difficult problem in intelligent video surveillance; whereas fusing multiple features has become a strong tool to solve it. In order to guide the fusion scheme, it is necessary to evaluate the matching performance of these features. In this paper, four typical features are chosen for the evaluation. They are the color histogram, UV chromaticity, major color spectrum histogram, and scale-invariant features (SIFT). Quantities of video data are collected to test their general accuracy, robustness, and real-time applicability. The robustness is measured under the conditions of illumination changes, Gaussian and salt noises,foreground errors, resolution changes, and camera angle differences. The experimental results show that the four features bear distinctive performances under the different conditions, which will provide important references for the feature fusion methods.