Action segmentation in untrimmed videos is essential for comprehensive video understanding. Despite significant progress in unsupervised methods, capturing both long-range dependencies and short-duration actions simultaneously remains a challenging task. To address this challenge, this paper introduces the Cluster-Refined Optimal Transport (CROT) method, combining hierarchical clustering and optimal transport for unsupervised action segmentation. We first hierarchically cluster video frame representations to capture long-range dependencies and generate pseudo-boundaries. Initial pseudo-labels are then obtained via optimal transport, ensuring short-duration actions are recognized. Finally, these pseudo-labels are refined using the pseudo-boundaries, resulting in the final segmentation output. Extensive experiments on three public datasets, i.e., YouTube Instructions, Breakfast, and 50Salads, demonstrate that our method performs on par with or better than previous approaches.

Temporal distribution shifts occur not only in low-dimensional time-series data but also in high-dimensional data like videos. This phenomenon leads to significant performance degeneration in video understanding methods such as streaming temporal action segmentation. To address this issue, we propose a flexible streaming temporal action segmentation model with diffusion models (FSTAS-DM). By utilizing streaming video clips with varying feature distributions as control conditions, our model can adapt to the shifts and inconsistency of the distribution between the training and testing domains. Additionally, we have introduced a multistage conditional control training strategy (MSCC), which enhances the temporal generalization ability of the model. Our method demonstrates commendable performance on datasets like GTEA, 50Salads, and Breakfast.

Recent methods relying on joint representation learning and clustering have demonstrated favorable outcomes in action segmentation. However, they face key limitations, including overlooking boundary actions with subtle differences and inadequate utilization of intermediate features. This renders these approaches less effective for segmentation tasks. Additionally, their high training costs hinder practical implementation. To address these issues, we propose a wavelet feature processing model (WFPM) that consists of two parts. Firstly, the WFPM incorporates wavelet feature noise reduction (WFNR). In this step, wavelet transformation is employed to distinguish local information from global information, thereby achieving the goal of reducing feature noise. Secondly, the model involves information entropy voting (IEV). Through this process, the importance of different levels is dynamically allocated, and the decomposition results are refined accordingly. Our experimental evaluation, which is founded on three evaluation criteria and conducted on four datasets, namely 50Salads, Breakfast, YouTube Instructional Videos, and MPII Cooking 2, shows that the proposed WFPM outperforms the existing state-of-the-art methods.

Multimodal emotion recognition (MER) has recently gained much attention since it can leverage information over multiple modalities. However, in real life, we often encounter the problem of missing modalities, as well as modeling the heterogeneity and correlation among multimodal data are challenges. To this end, we propose a unified model called cross-modal adaptive masked autoencoder (CMA-MAE) for incomplete multimodal learning. Our CMA-MAE model comprises a cross-modal adaptive fusion encoder (CMAFE) and a multiview adaptive encoder (MVAE) to capture and fuse the heterogeneity and correlation among multimodal features. Additionally, we design a convolutional decoder that progressive upsampling and fusion with the modality-invariant features to generate robust emotional features from partially observable data. To effectively utilize both data with complete and incomplete modalities for feature learning, we adopt an end-to-end approach that simultaneously optimizes classification and reconstruction tasks. Extensive testing on the DEAP and SEED-IV datasets is conducted to assess our model, with the findings demonstrating that our CMA-MAE model outperforms current leading approaches in both incomplete and complete multimodal learning scenarios.

Recently, deep learning-based remote sensing image super-resolution (RSISR) techniques have achieved significant progress, but challenges remain in preserving critical edge details essential for high-quality image reconstruction, These details are crucial for tasks like object recognition, change detection, and accurate analysis in remote sensing imagery. Furthermore, existing RSISR methods typically require substantial computational resources, making them unsuitable for resource-constrained edge devices. To address these challenges, we propose a novel Edge-Guided Super-Resolution Network (EGSRN). The network employs an Edge Extraction Module (Edge Net) to explicitly extract edge information from low-resolution images, combined with multi-layer Feature Extraction Modules (FEM) and an Edge Information Fusion (EIF) mechanism to progressively integrate edge and image features. This design enables precise recovery of edge details, significantly enhancing the overall visual quality of the reconstructed images. Edge-aware processing enhances visual fidelity while also improving the accuracy of downstream tasks, such as classification, object detection, and change analysis. Furthermore, the network incorporates lightweight designs such as depthwise separable convolutions and channel shuffling to effectively reduce computational demands. Comprehensive experiments were conducted on two remote sensing datasets, and the model’s parameter count and floating-point operations (FLOPs) were evaluated. Results demonstrate that the proposed method achieves an excellent balance between performance and model complexity, delivering superior super-resolution reconstruction quality while maintaining low computational costs, making it well-suited for resource-limited real-world applications.

Recently, deep learning-based remote sensing image super-resolution (RSISR) methods have achieved remarkable progress. However, effectively preserving high-frequency details remains a significant challenge, as these features are critical for downstream tasks such as object detection, change analysis, and scene classification. Moreover, relying solely on the information contained in low-resolution images often results in the loss of structural details, thereby degrading reconstruction quality. To address these issues, we propose a novel Wavelet-guided and Feature-Aware Super-Resolution Network (WFA-SRNet). The proposed network adopts a dual-branch architecture, consisting of a Feature Extraction Block (FEB) and a High-Frequency Extraction Block (HFE), to collaboratively model semantic structures and fine-grained textures. Specifically, FEB integrates a Shift-Window Cross Attention (SWCA) mechanism and a dictionary-based similarity matching strategy to capture non-local self-similarities, while the HFE branch incorporates a wavelet-domain high-frequency modeling module (WD-HFE), which explicitly decomposes and reconstructs frequency components via Discrete Wavelet Transform (DWT) and Inverse DWT (IDWT) to enhance edge and texture recovery. Furthermore, a Fusion Attention (FA) module is designed to guide the integration of multi-source features from both semantic and high-frequency pathways. Extensive experiments on multiple benchmark remote sensing datasets demonstrate that WFA-SRNet achieves superior reconstruction performance, particularly in restoring structural and textural details. Additionally, the proposed method significantly improves the accuracy of downstream classification tasks, showing strong potential for practical RSISR applications.

Recently, deep learning-based remote sensing image super-resolution (RSISR) has achieved remarkable progress. However, conventional super-resolution methods usually assume a fixed and known degradation process (e.g., bicubic downsampling), which often leads to significant performance degradation when applied to real-world data with diverse and unknown degradations. To overcome this limitation, we propose DCR-SRNet, a novel Degradation-Contrastive and Wavelet-Guided Network for blind RSISR. The proposed network incorporates three key innovations: First, we design a contrastive degradation representation learning strategy that disentangles degradation priors from scene semantics by pulling together representations of identical degradations across different scenes while pushing apart those of different degradations within the same scene. Second, we introduce a wavelet-guided patch-wise weighted loss module, which employs wavelet decomposition and patch-level discrimination scores to adaptively reweight the pixel-wise loss, thereby enhancing the recovery of edge and texture details. Third, we design an adaptive modulation block (AMB) that injects degradation priors into the reconstruction process through feature- and channel-wise modulation, enabling robust adaptation to diverse degradations. Extensive experiments on three benchmark remote sensing datasets demonstrate that DCR-SRNet significantly outperforms state-of-the-art methods, particularly in preserving structural and textural details.

Multi-modal Emotion Recognition (MER) has demonstrated competitive performance in affective computing, owing to synthesizing information from diverse modalities. However, many existing approaches still face unresolved challenges, such as: (i) how to learn compact yet representative features from multi-modal data simultaneously and (ii) how to address differences among subjects and enhance the generalization of the emotion recognition model, given the diverse nature of individual biological signals. To this end, we propose a Dynamic Interactive Network with Self-Distillation (DISD-Net) for cross-subject MER. The DISD-Net incorporates a dynamin interactive module to capture the intra- and inter-modal interactions from multi-modal data. Additionally, to enhance compactness in modal representations, we leverage the soft labels generated by the DISD-Net model as supplemental training guidance. This involves incorporating self-distillation, aiming to transfer the knowledge that the DISD-Net model contains hard and soft labels to each modality. Finally, domain adaptation (DA) is seamlessly integrated into the dynamic interactive and self-distillation components, forming a unified framework to extract subject-invariant multi-modal emotional features. Experimental results indicate that the proposed model achieves a mean accuracy of 75.00% with a standard deviation of 7.68% for the DEAP dataset and a mean accuracy of 65.65% with a standard deviation of 5.08% for the SEED-IV dataset.

The streaming temporal action segmentation (STAS) task, a supplementary task of temporal action segmentation (TAS), has not received adequate attention in the field of video understanding. Existing TAS methods are constrained to offline scenarios due to their heavy reliance on multimodal features and complete contextual information. The STAS task requires the model to classify each frame of the entire untrimmed video sequence clip by clip in time, thereby extending the applicability of TAS methods to online scenarios. However, directly applying existing TAS methods to SATS tasks results in significantly poor segmentation outcomes. In this article, we thoroughly analyze the fundamental differences between STAS tasks and TAS tasks, attributing the severe performance degradation when transferring models to model bias and optimization dilemmas. We introduce an end-to-end streaming video TAS model with reinforcement learning (SVTAS-RL). The end-to-end modeling method mitigates the modeling bias introduced by the change in task nature and enhances the feasibility of online solutions. Reinforcement learning (RL) is utilized to alleviate the optimization dilemma. Through extensive experiments, the SVTAS-RL model significantly outperforms existing STAS models and achieves competitive performance to the state-of-the-art (SOTA) TAS model on multiple datasets under the same evaluation criteria, demonstrating notable advantages on the ultralong video dataset EGTEA. Our code is publicly available at https://github.com/Thinksky5124/SVTAS.

The goal of unsupervised action segmentation (UAS) is to classify video frames into predefined action classes, which can be considered as a clustering or boundary detection problem. Previous research utilizing bottom-up agglomerative hierarchical clustering methods suffers from over-segmentation or under-segmentation. To address these problems, we propose the Two-step Temporal Divisive Clustering (TTDC) with two components. The first step of TTDC is top-down Temporal Divisive Clustering (TDC), which captures global contexts by comparing the intra-class variances of different classes, and captures local contexts through boundary detection. The second step is the Self-supervised Soft Boundary Regression Network (SS-BRN). SS-BRN is trained by soft pseudo-labels from TDC to refine the boundaries of clusters. In addition, to alleviate the issue of low confidence in pseudo-labels, we use a loss function with soft pseudo-labels. Our empirical evaluations on three benchmarks including 50Salads, Breakfast, and MPII Cooking 2 dataset demonstrate that TTDC outperforms the state-of-the-art methods.

Multimodal emotion recognition (MER) has attracted much attention since it can leverage consistency and complementary relationships across multiple modalities. However, previous studies mostly focused on the complementary information of multimodal signals, neglecting the consistency information of multimodal signals and the topological structure of each modality. To this end, we propose a dense graph convolution network (DGC) equipped with a joint cross attention (JCA), named DG-JCA, for MER. The main advantage of the DG-JCA model is that it simultaneously integrates the spatial topology, consistency, and complementarity of multimodal data into a unified network framework. Meanwhile, DG-JCA extends the graph convolution network (GCN) via a dense connection strategy and introduces cross attention to joint model well-learned features from multiple modalities. Specifically, we first build a topology graph for each modality and then extract neighborhood features of different modalities using DGC driven by dense connections with multiple layers. Next, JCA performs cross-attention fusion in intra- and intermodality based on each modality's characteristics while balancing the contributions of various modalities’ features. Finally, subject-dependent and subject-independent experiments on the DEAP and SEED-IV datasets are conducted to evaluate the proposed method. Abundant experimental results show that the proposed model can effectively extract and fuse multimodal features and achieve outstanding performance in comparison with some state-of-the-art approaches.

For RGB-based temporal action segmentation (TAS), excellent methods that capture frame-level features have achieved remarkable performance. However, for motion-centered TAS, it is still challenging for existing methods that ignore the extraction of spatial features of joints. In addition, inaccurate action boundaries caused by the frames of similar motion destroy the integrity of the action segments. To alleviate the issues, an end-to-end Involving Distinguished Temporal Graph Convolutional Networks called IDT-GCN is proposed. First, we construct an enhanced spatial graph structure that adaptively captures the similar and differential dependencies between joints in a single topology through learning two independent correlation modeling functions. Then, the proposed Involving Distinguished Graph Convolutional (ID-GC) models the spatial correlations of different actions in a video by using multiple enhanced topologies on the corresponding channels. Furthermore, we design a generic modeling temporal action regression network, termed Temporal Segment Regression (TSR), to extract segmented encoding features and action boundary representations by modeling action sequences. Combining them with label smoothing modules, we develop powerful spatial-temporal graph convolutional networks (IDT-GCN) for fine-grained TAS, which notably outperforms state-of-the-art methods on the MCFS-22 and MCFS-130 datasets. Adding TSR to TCN-based baseline methods achieves competitive performance compared with the state-of-the-art transformer-based methods on RGB-based datasets, i.e., Breakfast and 50Salads. Further experimental results on the action recognition task verify the superiority of the enhanced spatial graph structure over the previous graph convolutional networks.

The electroencephalogram (EEG) signal has become a highly effective decoding target for emotion recognition and has garnered significant attention from researchers. Its spatial topological and time-dependent characteristics make it crucial to explore both spatial information and temporal information for accurate emotion recognition. However, existing studies often focus on either spatial or temporal aspects of EEG signals, neglecting the joint consideration of both perspectives. To this end, this article proposes a hybrid network consisting of a dynamic graph convolution (DGC) module and temporal self-attention representation (TSAR) module, which concurrently incorporates the representative knowledge of spatial topology and temporal context into the EEG emotion recognition task. Specifically, the DGC module is designed to capture the spatial functional relationships within the brain by dynamically updating the adjacency matrix during the model training process. Simultaneously, the TSAR module is introduced to emphasize more valuable time segments and extract global temporal features from EEG signals. To fully exploit the interactivity between spatial and temporal information, the hierarchical cross-attention fusion (H-CAF) module is incorporated to fuse the complementary information from spatial and temporal features. Extensive experimental results on the DEAP, SEED, and SEED-IV datasets demonstrate that the proposed method outperforms other state-of-the-art methods.

In recent years, multiview learning technologies have attracted a surge of interest in the machine learning domain. However, when facing complex and diverse applications, most multiview learning methods mainly focus on specific fields rather than provide a scalable and robust proposal for different tasks. Moreover, most conventional methods used in these tasks are based on single view, which cannot be readily extended into the multiview scenario. Therefore, how to provide an efficient and scalable multiview framework is very necessary yet full of challenges. Inspired by the fact that most of the existing single view algorithms are graph-based ones to learn the complex structures within given data, this article aims at leveraging most existing graph embedding works into one formula via introducing the graph consensus term and proposes a unified and scalable multiview learning framework, termed graph consensus multiview framework (GCMF). GCMF attempts to make full advantage of graph-based works and rich information in the multiview data at the same time. On one hand, the proposed method explores the graph structure in each view independently to preserve the diversity property of graph embedding methods; on the other hand, learned graphs can be flexibly chosen to construct the graph consensus term, which can more stably explore the correlations among multiple views. To this end, GCMF can simultaneously take the diversity and complementary information among different views into consideration. To further facilitate related research, we provide an implementation of the multiview extension for locality linear embedding (LLE), named GCMF-LLE, which can be efficiently solved by applying the alternating optimization strategy. Empirical validations conducted on six benchmark datasets can show the effectiveness of our proposed method.

With the rapid development of facial tampering techniques, the deepfake detection task has attracted widespread social concerns. Most existing video-based methods adopt temporal convolution to learn temporal discontinuities directly, where they might neglect to explore both local detail mutation and inconsistent global expression semantics in the temporal dimension. This makes it difficult to learn more discriminative forgery cues. To mitigate this issue, we introduce a novel deepfake video detection framework specifically designed to capture fine-grained traces of tampering. Concretely, we first present a Multi-layered Feature Extraction module (MFE) that constructs comprehensive spatio-temporal representations by stitching different levels of features together. Afterward, we propose a Bidirectional temporal Artifact Enhancement module (BAE), which exploits local differences between adjacent frames to enhance frame-level features. Moreover, we present a Cross temporal Stride Aggregation strategy (CSA) to mine inconsistent global semantics and adaptively obtain multi-timescale representations. Extensive experiments on several benchmarks demonstrate that the proposed method outperforms state-of-the-art performance compared to other competitive approaches.

Single-frame temporal action localization aims to predict the start time, end time, and categories of action instances in untrimmed videos with only one timestamp label for each action instance. However, recent works have two challenges: incomplete location results caused by various same-class action snippets (or frames) and redundant predictions due to ambiguous background snippets. To tackle the above issues, we design a model consisting of an action reinforcement module (ARM) and an action indication module (AIM). Specifically, the ARM improves the model's generalization ability by augmenting local and global features, thus solving the challenges of incomplete predictions. Simultaneously, the AIM detects the frames that indicate the location of the action instances (called keyframes) in the training period. Then the AIM applies the detected keyframes to filter redundant prediction results in the testing period. Extensive experiments are performed on two benchmark datasets, THUMOS-14 and ActivityNet-v1.3, demonstrating that the model could achieve state-of-the-art performance compared to some competitive approaches.

Multi-metric learning -a method to learn multiple local metrics to reveal the feature's correlations of samples from different local regions-has become an essential tool to measure the similarities between instances from heterogeneous datasets. However, most existing cluster-based MML methods first partition the training data with a predefined metric and then learn multiple metrics via the local instances, leading to these two independent procedures fail to cooperate with each other. In this paper, we propose an Optimal instance Partition-based Multi-Metric Learning (OPM 2 L) method for heterogeneous dataset classification by unifying the instance partition and multiple local metrics learning into a single objective. In particular, multiple anchor centers together with a global metric are employed to assist the instance partition process. During the training, the shared information contained in local metrics is aggregated into the global metric by a dedicated regularizer, which improves the instance partition process and offers the subsequent multiple local metrics learning with more informative instances. Moreover, an efficient alternating direction technology is employed to seek a feasible solution to the proposed method. We further confirmed that the sub-problems can be settled with closed-form solutions, while the superiority of the proposed method is also proved by experimental results on extensive datasets.

Due to the limitation of imaging equipment, it is difficult to acquire hyperspectral images with high spatial resolution directly. Existing approaches improve the resolution of HSIs by fusing multispectral image (MSI) and hyperspectral image (HSI). However, most of them are only feed-forward. They only learn low- to high-resolution feature mappings without considering the ill-posedness of super-resolution tasks, leading to a large solution space of mapping functions and making it difficult to learn a complete mapping function. Moreover, there is a large resolution difference between HSI and MSI, and some up-sampling operations are inevitably employed in the network. Nevertheless, traditional upsampling methods only represent pixel points in a discrete way, failing to adequately restore the continuous spatial and spectral information. To this end, this paper proposes a spatial-spectral implicit neural representation network for hyperspectral and multispectral image fusion (SS-INR). Inspired by the success of implicit neural representation(INR) in continuum reconstruction, we design spatial-INR and spectral-INR for spatial and spectral resolution reconstruction, respectively. SS-INR contains two processes: forward fusion (FF) and back-projection fusion(BPF). In the FF process, the input HSI is first spatially upsampled with Spatial-INR to overcome spatial resolution differences while performing initial fusion with MSI. In the BPF process, we explore the spatial and spectral degradation processes and use them as prior knowledge for error correction. Extensive experiments on five public hyperspectral datasets demonstrate the effectiveness of SS-INR, and SS-INR achieves competitive results compared with existing state-of-the-art fusion methods. The source code for SS-INR will be released at https://github.com/wxy11-27/SS-INR.

The common goal of the studies is to map any emotional states encoded from electroencephalogram (EEG) into 2-dimensional arousal-valance scores. It is still challenging due to each emotion having its specific spatial structure and dynamic dependence over the distinct time segments among EEG signals. This paper aims to model human dynamic emotional behavior by considering the location connectivity and context dependency of brain electrodes. Thus, we designed a hybrid EEG modeling method that mainly adopts the attention mechanism, combining a multi-domain spatial transformer (MST) module and a dynamic temporal transformer (DTT) module, named MSDTTs. Specifically, the MST module extracts single-domain and cross-domain features from different brain regions and fuses them into multi-domain spatial features. Meanwhile, the temporal dynamic excitation (TDE) is inserted into the multi-head convolutional transformer to form the DTT module. These two blocks work together to activate and extract the emotion-related dynamic temporal features within the DTT module. Furthermore, we place the convolutional mapping into the transformer structure to mine the static context features among the keyframes. Overall results show that high classification accuracy of 98.91%/0.14% was obtained by the $\beta$ frequency band of the DEAP dataset, and 97.52%/0.12% and 96.70%/0.26% were obtained by the $\gamma$ frequency band of SEED and SEED-IV datasets. Empirical experiments indicate that our proposed method can achieve remarkable results in comparison with state-of-the-art algorithms.

Multi-view clustering intent to separate data into different groups regarding their multiple traits. Existing tensor multi-view clustering techniques can explore the high-order associations of multi-perspective characteristics. However, they suffer from the following issues: (1) data features and local geometric structures in nonlinear subspace are often ignored; (2) the prior knowledge of singular values in the tensor kernel norm is not well utilized. To settle these problems, we propose a novel Markov chain tensor-based approach named Tensorized Multi-view Clustering via Hyper-graph Regularization(TMC-HR). Firstly, the third-tensor based on Markov chain transition probability is constructed and rotated to reduce the model complexity. Secondly, hyper-graph regularization is employed to save the high-order local geometrical structure imbedded in the original space. Thirdly, the weighted strategy is applied to the tensor composed of latent representations to extract the high-order relationships and diverse information between different views. Finally, an effective iterative method is utilized to solve the proposed TMC-HR. We conducted extensive experiments on benchmark datasets corresponding to different types to indicate that TMC-HR performs superior over other multi-view clustering approaches.

The dynamic uncertain relationship among each brain region is a necessary factor that limits EEG-based emotion recognition. It is a thought-provoking problem to availably employ time-varying spatial and temporal characteristics from multi-channel electroencephalogram (EEG) signals. Although deep learning has made remarkable achievements in emotion recognition, the biological topological information among brain regions does not fully exploit, which is vital for EEG-based emotion recognition. In response to this problem, we design a hybrid model called ST-GCLSTM, which comprises a spatial-graph convolutional network (SGCN) module and an attention-enhanced bi-directional Long Short-Term Memory (LSTM) module. The main advantage of ST-GCLSTM is that it can consider the biological topology information of each brain region to extract representative spatial-temporal features from multiple EEG channels. Specifically, we construct two layers SGCN by introducing adjacency matrices to adaptively learn the intrinsic connection among different EEG channels. Moreover, an attention-enhanced mechanism is placed into a bi-directional LSTM module to extract the crucial spatial-temporal features from sequential EEG data, and then these features serve as the input layer of the classifier to learn discriminative emotion-related features. Extensive experiments on the DEAP, SEED, and SEED-IV datasets demonstrate the effectiveness of the proposed ST-GCLSTM model, revealing that our model had an absolute performance improvement over state-of-the-art strategies.

A single feature is hard to describe the content of images from an overall perspective, which limits the retrieval performances of single-feature-based methods in image retrieval tasks. To fully describe the properties of images and improve the retrieval performances, multifeature fusion ranking-based methods are proposed. However, the effectiveness of multifeature fusion in image retrieval has not been theoretically explained. This article gives a theoretical proof to illustrate the role of independent features in improving the retrieval results. Based on the theoretical proof, the original ranking list generated with a single feature greatly influences the performances of multifeature fusion ranking. Inspired by the principle of three degrees of influence in social networks, this article proposes a reranking method named k -nearest neighbors' neighbors' neighbors' graph (N3G) to improve the original ranking list by a single feature. Furthermore, a multigraph fusion ranking (MFR) method motivated by the group relation theory in social networks for multifeature ranking is also proposed, which considers the correlations of all images in multiple neighborhood graphs. Evaluation experiments conducted on several representative data sets (e.g., UK-bench, Holiday, Corel-10K, and Cifar-10) validate that N3G and MFR outperform the other state-of-the-art methods.

In many real-world domains, datasets with imbalanced class distributions occur frequently, which may confuse various machine learning tasks. Among all these tasks, learning classifiers from imbalanced datasets is an important topic. To perform this task well, it is crucial to train a distance metric which can accurately measure similarities between samples from imbalanced datasets. Unfortunately, existing distance metric methods, such as large margin nearest neighbor, information-theoretic metric learning, etc., care more about distances between samples and fail to take imbalanced class distributions into consideration. Traditional distance metrics have natural tendencies to favor the majority classes, which can more easily satisfy their objective function. Those important minority classes are always neglected during the construction process of distance metrics, which severely affects the decision system of most classifiers. Therefore, how to learn an appropriate distance metric which can deal with imbalanced datasets is of vital importance, but challenging. In order to solve this problem, this paper proposes a novel distance metric learning method named distance metric by balancing KL-divergence (DMBK). DMBK defines normalized divergences using KL-divergence to describe distinctions between different classes. Then it combines geometric mean with normalized divergences and separates samples from different classes simultaneously. This procedure separates all classes in a balanced way and avoids inaccurate similarities incurred by imbalanced class distributions. Various experiments on imbalanced datasets have verified the excellent performance of our novel method.

Human action segmentation is important for human action analysis, which is a highly active research area. Most segmentation methods are based on clustering or numerical descriptors, which are only related to data, and consider no relationship between the data and physical characteristics of human actions. Physical characteristics of human motions are those that can be directly perceived by human beings, such as speed, acceleration, continuity, and so on, which are quite helpful in detecting human motion segment points. We propose a new physical-based descriptor of human action by curvature sequence warp space alignment (CSWSA) approach for sequence segmentation in this paper. Furthermore, time series-warp metric curvature segmentation method is constructed by the proposed descriptor and CSWSA. In our segmentation method, descriptor can express the changes of human actions, and CSWSA is an auxiliary method to give suggestions for segmentation. The experimental results show that our segmentation method is effective in both CMU human motion and video-based data sets.

Along with the arrival of multimedia time, multimedia data has replaced textual data to transfer information in various fields. As an important form of multimedia data, images have been widely utilized by many applications, such as face recognition and image classification. Therefore, how to accurately annotate each image from a large set of images is of vital importance but challenging. To perform these tasks well, it is crucial to extract suitable features to character the visual contents of images and learn an appropriate distance metric to measure similarities between all images. Unfortunately, existing feature operators, such as histogram of gradient, local binary pattern, and color histogram, care more about the visual character of images and lack the ability to distinguish semantic information. Similarities between those features cannot reflect the real category correlations due to the well-known semantic gap. In order to solve this problem, this paper proposes a regularized distance metric framework called semantic discriminative metric learning (SDML). SDML combines geometric mean with normalized divergences and separates images from different classes simultaneously. The learned distance metric can treat all images from different classes equally. And distinctions between similar classes with entirely different semantic contents are emphasized by SDML. This procedure ensures the consistency between dissimilarities and semantic distinctions and avoids inaccuracy similarities incurred by unbalanced locations of samples. Various experiments on benchmark image datasets show the excellent performance of the novel method.

In recent years, the theory of sparse representation (SR) has been widely exploited in sparse subspace learning (SSL). Among all these methods, SR is a parameter-free global algorithm in nature which is mostly utilized to construct the correlations between samples to avoid some negative effects incurred by k-nearest neighbor (KNN) or some other methods. However, these SSL algorithms always lack obvious discrimination because of the ignorance of samples distribution. Meanwhile, some incorrect correlations are taken into consideration owing to the global feature of SR. To solve these two problems, a new SSL algorithm called locality structured sparsity preserving embedding (LSPE) is proposed in this paper. We add the local structured information to SR and construct correlations between samples. However, LSPE is an unsupervised method which wastes all label information. Therefore, LSPE is extended to semi-supervised LSPE (SLSPE) in this paper. SLSPE not only makes good use of the label information but also enhances the discriminative power of LSPE. Extensive experiments have been performed on three image datasets (CMU, COIL20, ORL) and two UCI datasets (Glass, Segment) to prove the efficiency of the LSPE and SLSPE.

Subspace selection is widely applied in data classification, clustering, and visualization. The samples projected into subspace can be processed efficiently. In this paper, we research the linear discriminant analysis (LDA) and maximum margin criterion (MMC) algorithms intensively and analyze the effects of scatters to subspace selection. Meanwhile, we point out the boundaries of scatters in LDA and MMC algorithms to illustrate the differences and similarities of subspace selection in different circumstances. Besides, the effects of outlier classes on subspace selection are also analyzed. According to the above analysis, we propose a new subspace selection method called angle linear discriminant embedding (ALDE) on the basis of angle measurement. ALDE utilizes the cosine of the angle to get new within-class and between-class scatter matrices and avoids the small sample size problem simultaneously. To deal with high-dimensional data, we extend ALDE to a two-stage ALDE (TS-ALDE). The synthetic data experiments indicate that ALDE can balance the within-class and between-class scatters and be robust to outlier classes. The experimental results based on UCI machine-learning repository and image databases show that TS-ALDE has a lower time complexity than ALDE while processing high-dimensional data.

The coverage problem of networks is one of the key issues researched in wireless sensor networks (WSNs). To find the optimal coverage solution of sensor networks is a pressing concern now. The terrain of the detection area is often more complex in the applications of three-dimensional sensor networks. In this paper, a new network coverage and optimization control strategy based on genetic algorithm is proposed to solve the deterministic coverage problem of sensor nodes. The fitness function of the associated genetic algorithm is determined on a two-dimensional plane and iterations are utilized to find optimal solutions. The simulations and results indicate that the coverage strategy is an efficient coverage strategy for the three-dimensional terrain.

High utility itemsets mining is a hot topic in data stream mining. It is essential that the mining algorithm should be efficient in both time and space for data stream is continuous and unbounded. To the best of our knowledge, the existing algorithms