Enhancing Single-Frame Supervision for Better Temporal Action Localization.

Temporal action localization aims to identify the boundaries and categories of actions in videos, such as scoring a goal in a football match. Single-frame supervision has emerged as a labor-efficient way to train action localizers as it requires only one annotated frame per action. However, it often suffers from poor performance due to the lack of precise boundary annotations. To address this issue, we propose a visual analysis method that aligns similar actions and then propagates a few user-provided annotations (e.g., boundaries, category labels) to similar actions via the generated alignments. Our method models the alignment between actions as a heaviest path problem and the annotation propagation as a quadratic optimization problem. As the automatically generated alignments may not accurately match the associated actions and could produce inaccurate localization results, we develop a storyline visualization to explain the localization results of actions and their alignments. This visualization facilitates users in correcting wrong localization results and misalignments. The corrections are then used to improve the localization results of other actions. The effectiveness of our method in improving localization performance is demonstrated through quantitative evaluation and a case study.

Knowledge Conditioned Variational Learning for One-Class Facial Expression Recognition.

The openness of application scenarios and the difficulties of data collection make it impossible to prepare all kinds of expressions for training. Hence, detecting expression absent during the training (called alien expression) is important to enhance the robustness of the recognition system. So in this paper, we propose a facial expression recognition (FER) model, named OneExpressNet, to quantify the probability that a test expression sample belongs to the distribution of training data. The proposed model is based on variational auto-encoder and enjoys several merits. First, different from conventional one class classification protocol, OneExpressNet transfers the useful knowledge from the related domain as a constraint condition of the target distribution. By doing so, OneExpressNet will pay more attention to the descriptive region for FER. Second, features from both source and target tasks will aggregate after constructing a skip connection between the encoder and decoder. Finally, to further separate alien expression from training expression, empirical compact variation loss is jointly optimized, so that training expression will concentrate on the compact manifold of feature space. The experimental results show that our method can achieve state-of-the-art results in one class facial expression recognition on small-scale lab-controlled datasets including CFEE and KDEF, and large-scale in-the-wild datasets including RAF-DB and ExpW.

Exploring High-order Spatio-temporal Correlations from Skeleton for Person Re-identification.

Person re-identification (Re-ID) has become a hot research topic due to its widespread applications. Conducting person Re-ID in video sequences is a practical requirement, in which the crucial challenge is how to pursue a robust video representation based on spatial and temporal features. However, most of the previous methods only consider how to integrate part-level features in the spatio-temporal range, while how to model and generate the part-correlations is little exploited. In this paper, we propose a skeleton-based dynamic hypergraph framework, namely Skeletal Temporal Dynamic Hypergraph Neural Network (ST-DHGNN) for person Re-ID, which resorts to modeling the high-order correlations among various body parts based on a time series of skeletal information. Specifically, multi-shape and multi-scale patches are heuristically cropped from feature maps, constituting spatial representations in different frames. A joint-centered hypergraph and a bone-centered hypergraph are constructed in parallel from multiple body parts (i.e., head, trunk, and legs) with spatio-temporal multi-granularity in the entire video sequence, in which the graph vertices representing regional features and hyperedges denoting relationships. Dynamic hypergraph propagation containing the re-planning module and the hyperedge elimination module is proposed to better integrate features among vertices. Feature aggregation and attention mechanisms are also adopted to obtain a better video representation for person Re-ID. Experiments show that the proposed method performs significantly better than the state-of-the-art on three video-based person Re-ID datasets, including iLIDS-VID, PRID-2011, and MARS.

Cost-Sensitive Hypergraph Learning With F-Measure Optimization.

The imbalanced issue among data is common in many machine-learning applications, where samples from one or more classes are rare. To address this issue, many imbalanced machine-learning methods have been proposed. Most of these methods rely on cost-sensitive learning. However, we note that it is infeasible to determine the precise cost values even with great domain knowledge for those cost-sensitive machine-learning methods. So in this method, due to the superiority of F-measure on evaluating the performance of imbalanced data classification, we employ F-measure to calculate the cost information and propose a cost-sensitive hypergraph learning method with F-measure optimization to solve the imbalanced issue. In this method, we employ the hypergraph structure to explore the high-order relationships among the imbalanced data. Based on the constructed hypergraph structure, we optimize the cost value with F-measure and further conduct cost-sensitive hypergraph learning with the optimized cost information. The comprehensive experiments validate the effectiveness of the proposed method.

Graph Learning on Millions of Data in Seconds: Label Propagation Acceleration on Graph Using Data Distribution.

Graph-based semi-supervised learning methods have been used in a wide range of real-world applications, e.g., from social relationship mining to multimedia classification and retrieval. However, existing methods are limited along with high computational complexity or not facilitating incremental learning, which may not be powerful to deal with large-scale data, whose scale may continuously increase, in real world. This paper proposes a new method called Data Distribution Based Graph Learning (DDGL) for semi-supervised learning on large-scale data. This method can achieve a fast and effective label propagation and supports incremental learning. The key motivation is to propagate the labels along smaller-scale data distribution model parameters, rather than directly dealing with the raw data as previous methods, which accelerate the data propagation significantly. It also improves the prediction accuracy since the loss of structure information can be alleviated in this way. To enable incremental learning, we propose an adaptive graph updating strategy which can update the model when there is distribution bias between new data and the already seen data. We have conducted comprehensive experiments on multiple datasets with sample sizes increasing from seven thousand to five million. Experimental results on the classification task on large-scale data demonstrate that our proposed DDGL method improves the classification accuracy by a large margin while consuming much less time compared to state-of-the-art methods.

Structure Evolution on Manifold for Graph Learning.

Graph has been widely used in various applications, while how to optimize the graph is still an open question. In this paper, we propose a framework to optimize the graph structure via structure evolution on graph manifold. We first define the graph manifold and search the best graph structure on this manifold. Concretely, associated with the data features and the prediction results of a given task, we define a graph energy to measure how the graph fits the graph manifold from an initial graph structure. The graph structure then evolves by minimizing the graph energy. In this process, the graph structure can be evolved on the graph manifold corresponding to the update of the prediction results. Alternatively iterating these two processes, both the graph structure and the prediction results can be updated until converge. It achieves the suitable structure for graph learning without searching all hyperparameters. To evaluate the performance of the proposed method, we have conducted experiments on eight datasets and compared with the recent state-of-the-art methods. Experiment results demonstrate that our method outperforms the state-of-the-art methods in both transductive and inductive settings.

Hypergraph Learning: Methods and Practices.

Hypergraph learning is a technique for conducting learning on a hypergraph structure. In recent years, hypergraph learning has attracted increasing attention due to its flexibility and capability in modeling complex data correlation. In this paper, we first systematically review existing literature regarding hypergraph generation, including distance-based, representation-based, attribute-based, and network-based approaches. Then, we introduce the existing learning methods on a hypergraph, including transductive hypergraph learning, inductive hypergraph learning, hypergraph structure updating, and multi-modal hypergraph learning. After that, we present a tensor-based dynamic hypergraph representation and learning framework that can effectively describe high-order correlation in a hypergraph. To study the effectiveness and efficiency of hypergraph generation and learning methods, we conduct comprehensive evaluations on several typical applications, including object and action recognition, Microblog sentiment prediction, and clustering. In addition, we contribute a hypergraph learning development toolkit called THU-HyperG.

Kullback-Leibler Divergence Metric Learning.

The Kullback-Leibler divergence (KLD), which is widely used to measure the similarity between two distributions, plays an important role in many applications. In this article, we address the KLD metric-learning task, which aims at learning the best KLD-type metric from the distributions of datasets. Concretely, first, we extend the conventional KLD by introducing a linear mapping and obtain the best KLD to well express the similarity of data distributions by optimizing such a linear mapping. It improves the expressivity of data distribution, which means it makes the distributions in the same class close and those in different classes far away. Then, the KLD metric learning is modeled by a minimization problem on the manifold of all positive-definite matrices. To deal with this optimization task, we develop an intrinsic steepest descent method, which preserves the manifold structure of the metric in the iteration. Finally, we apply the proposed method along with ten popular metric-learning approaches on the tasks of 3-D object classification and document classification. The experimental results illustrate that our proposed method outperforms all other methods.

View-Aware Geometry-Structure Joint Learning for Single-View 3D Shape Reconstruction.

Reconstructing a 3D shape from a single-view image using deep learning has become increasingly popular recently. Most existing methods only focus on reconstructing the 3D shape geometry based on image constraints. The lack of explicit modeling of structure relations among shape parts yields low-quality reconstruction results for structure-rich man-made shapes. In addition, conventional 2D-3D joint embedding architecture for image-based 3D shape reconstruction often omits the specific view information from the given image, which may lead to degraded geometry and structure reconstruction. We address these problems by introducing VGSNet, an encoder-decoder architecture for view-aware joint geometry and structure learning. The key idea is to jointly learn a multimodal feature representation of 2D image, 3D shape geometry and structure so that both geometry and structure details can be reconstructed from a single-view image. To this end, we explicitly represent 3D shape structures as part relations and employ image supervision to guide the geometry and structure reconstruction. Trained with pairs of view-aligned images and 3D shapes, the VGSNet implicitly encodes the view-aware shape information in the latent feature space. Qualitative and quantitative comparisons with the state-of-the-art baseline methods as well as ablation studies demonstrate the effectiveness of the VGSNet for structure-aware single-view 3D shape reconstruction.

Exploring complex and heterogeneous correlations on hypergraph for the prediction of drug-target interactions.

The continuous emergence of drug-target interaction data provides an opportunity to construct a biological network for systematically discovering unknown interactions. However, this is challenging due to complex and heterogeneous correlations between drug and target. Here, we describe a heterogeneous hypergraph-based framework for drug-target interaction (HHDTI) predictions by modeling biological networks through a hypergraph, where each vertex represents a drug or a target and a hyperedge indicates existing similar interactions or associations between the connected vertices. The hypergraph is then trained to generate suitably structured embeddings for discovering unknown interactions. Comprehensive experiments performed on four public datasets demonstrate that HHDTI achieves significant and consistently improved predictions compared with state-of-the-art methods. Our analysis indicates that this superior performance is due to the ability to integrate heterogeneous high-order information from the hypergraph learning. These results suggest that HHDTI is a scalable and practical tool for uncovering novel drug-target interactions.

[Case control study on anatomical locking plate and ordinary steel plate for the treatment of closed calcaneal fracture with Sandersâ ¡and â ¢].

OBJECTIVE: To compare clinical efficacy between anatomical locking plate (ALP) and ordinary steel plate (OSP) in treating closed calcaneal fractures with SandersⅡ and Ⅲ. METHODS: From May 2016 to May 2018, 68 patients with closed Sanders typeⅡ and Ⅲ calcaneal fractures were retrospectively analyzed, and were divided into anatomical locking plate group (ALP group) and ordinary steel plate group (OSP group) according to two kinds of plate fixation, and 34 patients in each group. In ALP group, there were 21 males and 13 females aged from 20 to 63 years old with average of (35.16±8.45) years old; 14 patients were typeⅡand 20 patients were type Ⅲaccording to Sanders classification;treated with ALP. In OSP group, there were 20 males and 14 females aged from 19 to 63 years old with average of (35.05±8.39) years old;19 patients were typeⅡand 15 patients were type Ⅲ according to Sanders classification;treated with OSP. Operative time, intraoperative blood loss and complications between two groups were observed and compared;preoperative and postoperative Böhler angle and gissane angle were also compared;American Orthopaedic Foot & Ankle Society (AOFAS) ankle and hind foot scores, foot and ankle disability index (FADI) scores were applied to evaluate clinical effect. RESULTS: All patients were followed up from 11 to 14 months with an average of (12.06±0.81) months. There were no statistical differences in opertive time, intraoperative blood loss, incision infection and refracture rate in complications between two groups (P>0.05);while there was significant difference in the number of screw loosening (P<0.05). Böhler angle and Gissane angle in ALP group at 6 and 12 months after opertaion were higher than that of OSP group (P<0.05), and the degree of improvement of Böhler angle and Gissane angle in ALP group were also higher than that of OSP group (P<0.05). Postopertaive AOFAS score and FADI score at 6 and 12 months in ALP group were higher than that of OSP group (P<0.05), while no statistical difference in AOFAS grading between two groups(P>0.05). CONCLUSION: Compared with OSP, ALP in treating SandersⅡ and Ⅲ calcaneal fractures could achieve better therapeutic effect, avoid screw loosening, reduce complications, and improve limb function in further.

Effect of intensity-modulated radiation therapy on sciatic nerve injury caused by echinococcosis.

Conventional radiotherapy has a good killing effect on femoral echinococcosis. However, the sciatic nerve around the lesion is irreversibly damaged owing to bystander effects. Although intensity-modulated radiation therapy shows great advantages for precise dose distribution into lesions, it is unknown whether intensity-modulated radiation therapy can perfectly protect the surrounding sciatic nerve on the basis of good killing of femoral echinococcosis foci. Therefore, this study comparatively analyzed differences between intensity-modulated radiation therapy and conventional radiotherapy on the basis of safety to peripheral nerves. Pure-breed Meriones meridiani with bilateral femoral echinococcosis were selected as the research object. Intensity-modulated radiation therapy was used to treat left femoral echinococcosis of Meriones meridianus, while conventional radiotherapy was used to treat right femoral echinococcosis of the same Meriones meridianus. The total radiation dose was 40 Gy. To understand whether intensity-modulated radiation therapy and conventional radiotherapy can kill femoral echinococcosis, trypan blue staining was used to detect pathological changes of bone Echinococcus granulosus and protoscolex death after radiotherapy. Additionally, enzyme histochemical staining was utilized to measure acid phosphatase activity in the protoscolex after radiotherapy. One week after radiotherapy, the overall structure of echinococcosis in bilateral femurs of Meriones meridiani treated by intensity-modulated radiation therapy disappeared. There was no significant difference in the mortality rate of protoscoleces of Echinococcus granulosus between the bilateral femurs of Meriones meridiani. Moreover, there was no significant difference in acid phosphatase activity in the protoscolex of Echinococcus granulosus between bilateral femurs. To understand the injury of sciatic nerve surrounding the foci of femoral echinococcosis caused by intensity-modulated radiation therapy and conventional radiotherapy, the ultrastructure of sciatic nerves after radiotherapy was observed by transmission electron microscopy. Additionally, apoptosis of neurons was examined using a terminal-deoxynucleotidyl transferase-mediated dUTP nick end labeling assay, and expression of Bcl-2 and Bax in sciatic nerve tissue was detected by immunohistochemical staining and western blot assay. Our results showed that most neurons in the left sciatic nerve of Meriones meridiani with echinococcosis treated by intensity-modulated radiation therapy had reversible injury, and there was no obvious apoptosis. Compared with conventional radiotherapy, the number of apoptotic cells and Bax expression in sciatic nerve treated by intensity-modulated radiation therapy were significantly decreased, while Bcl-2 expression was significantly increased. Our findings suggest that intensity-modulated radiation therapy has the same therapeutic effect on echinococcosis as conventional radiotherapy, and can reduce apoptosis of the sciatic nerve around foci caused by radiotherapy. Experiments were approved by the Animal Ethics Committee of People's Hospital of Xinjiang Uygur Autonomous Region, China (Approval No. 20130301A41) on March 1, 2013.

CPS-Based Self-Adaptive Collaborative Control for Smart Production-Logistics Systems.

Discrete manufacturing systems are characterized by dynamics and uncertainty of operations and behavior due to exceptions in production-logistics synchronization. To deal with this problem, a self-adaptive collaborative control (SCC) mode is proposed for smart production-logistics systems to enhance the capability of intelligence, flexibility, and resilience. By leveraging cyber-physical systems (CPSs) and industrial Internet of Things (IIoT), real-time status data are collected and processed to perform decision making and optimization. Hybrid automata is used to model the dynamic behavior of physical manufacturing resources, such as machines and vehicles in shop floors. Three levels of collaborative control granularity, including nodal SCC, local SCC, and global SCC, are introduced to address different degrees of exceptions. Collaborative optimization problems are solved using analytical target cascading (ATC). A proof of concept simulation based on a Chinese aero-engine manufacturer validates the applicability and efficiency of the proposed method, showing reductions in waiting time, makespan, and energy consumption with reasonable computational time. This article potentially enables manufacturers to implement CPS and IIoT in manufacturing environments and build up smart, flexible, and resilient production-logistics systems.

Hypergraph-Induced Convolutional Networks for Visual Classification.

At present, convolutional neural networks (CNNs) have become popular in visual classification tasks because of their superior performance. However, CNN-based methods do not consider the correlation of visual data to be classified. Recently, graph convolutional networks (GCNs) have mitigated this problem by modeling the pairwise relationship in visual data. Real-world tasks of visual classification typically must address numerous complex relationships in the data, which are not fit for the modeling of the graph structure using GCNs. Therefore, it is vital to explore the underlying correlation of visual data. Regarding this issue, we propose a framework called the hypergraph-induced convolutional network to explore the high-order correlation in visual data during deep neural networks. First, a hypergraph structure is constructed to formulate the relationship in visual data. Then, the high-order correlation is optimized by a learning process based on the constructed hypergraph. The classification tasks are performed by considering the high-order correlation in the data. Thus, the convolution of the hypergraph-induced convolutional network is based on the corresponding high-order relationship, and the optimization on the network uses each data and considers the high-order correlation of the data. To evaluate the proposed hypergraph-induced convolutional network framework, we have conducted experiments on three visual data sets: the National Taiwan University 3-D model data set, Princeton Shape Benchmark, and multiview RGB-depth object data set. The experimental results and comparison in all data sets demonstrate the effectiveness of our proposed hypergraph-induced convolutional network compared with the state-of-the-art methods.

Inductive Multi-Hypergraph Learning and Its Application on View-Based 3D Object Classification.

The wide 3D applications have led to increasing amount of 3D object data, and thus effective 3D object classification technique has become an urgent requirement. One important and challenging task for 3D object classification is how to formulate the 3D data correlation and exploit it. Most of the previous works focus on learning optimal pairwise distance metric for object comparison, which may lose the global correlation among 3D objects. Recently, a transductive hypergraph learning has been investigated for classification, which can jointly explore the correlation among multiple objects, including both the labeled and unlabeled data. Although these methods have shown better performance, they are still limited due to 1) a considerable amount of testing data may not be available in practice and 2) the high computational cost to test new coming data. To handle this problem, considering the multi-modal representations of 3D objects in practice, we propose an inductive multi-hypergraph learning algorithm, which targets on learning an optimal projection for the multi-modal training data. In this method, all the training data are formulated in multi-hypergraph based on the features, and the inductive learning is conducted to learn the projection matrices and the optimal multi-hypergraph combination weights simultaneously. Different from the transductive learning on hypergraph, the high cost training process is off-line, and the testing process is very efficient for the inductive learning on hypergraph. We have conducted experiments on two 3D benchmarks, i.e., the NTU and the ModelNet40 data sets, and compared the proposed algorithm with the state-of-the-art methods and traditional transductive multi-hypergraph learning methods. Experimental results have demonstrated that the proposed method can achieve effective and efficient classification performance. We also note that the proposed method is a general framework and has the potential to be applied in other applications in practice.

IPAD: Intensity Potential for Adaptive De-Quantization.

Display devices at bit depth of 10 or higher have been mature but the mainstream media source is still at bit depth of eight. To accommodate the gap, the most economic solution is to render source at low bit depth for high bit-depth display, which is essentially the procedure of de-quantization. Traditional methods, such as zero-padding or bit replication, introduce annoying false contour artifacts. To better estimate the least-significant bits, later works use filtering or interpolation approaches, which exploit only limited neighbor information, cannot thoroughly remove the false contours. In this paper, we propose a novel intensity potential (IP) field to model the complicated relationships among pixels. The potential value decreases as the spatial distance to the field source increases and the potentials from different field sources are additive. Based on the proposed IP field, an adaptive de-quantization procedure is then proposed to convert low-bit-depth images to high-bit-depth ones. To the best of our knowledge, this is the first attempt to apply potential field for natural images. The proposed potential field preserves local consistency and models the complicated contexts well. Extensive experiments on natural, synthetic, and high-dynamic range image data sets validate the efficiency of the proposed IP field. Significant improvements have been achieved over the state-of-the-art methods on both the peak signal-to-noise ratio and the structural similarity.

Intermediate-calcium based cementitious materials prepared by MSWI fly ash and other solid wastes: hydration characteristics and heavy metals solidification behavior.

Municipal solid waste incineration (MSWI) fly ash is a by-product of garbage incineration power generation, and its disposal is currently a world problem because it contains over standard heavy metals. This research aims to solidify the heavy metals in MSWI fly ash and make it to be utilizable construction materials under the guidance of intermediate-calcium cementitious materials (ICCM), and meanwhile figure out the solidification and hydration mechanism. The hydration characteristics of ICCM were characterized by XRD, FTIR, 29Si MAS-NMR and SEM techniques, and the environmental properties are investigated by TCLP and EPMA. The results indicate that the optimal ratio of (CaO + MgO)/(SiO2 + Al2O3) for ICCM is at the range of 0.76-0.88. The compressive strengths of ICCM reach the 42.5R normal Portland cement level, and the leaching concentrations of heavy metals meet the Chinese integrated wastewater discharge standard GB 8978-1996. As predominant hydration products, ettringite, hydrocalumite and amorphous C-S-H gel are principally responsible for the strength development of ICCM, and the (Ca + Mg)/(Si + Al) ratio at 0.88 has the best polymerized structure. The heavy metals are well solidified through combining with the C-S-H gel or absorbed in the hydration pastes. This paper provides an effective solution to use the MSWI fly ash in building material.

Beyond Pairwise Matching: Person Reidentification via High-Order Relevance Learning.

Person reidentification has attracted extensive research efforts in recent years. It is challenging due to the varied visual appearance from illumination, view angle, background, and possible occlusions, leading to the difficulties when measuring the relevance, i.e., similarities, between probe and gallery images. Existing methods mainly focus on pairwise distance metric learning for person reidentification. In practice, pairwise image matching may limit the data for comparison (just the probe and one gallery subject) and yet lead to suboptimal results. The correlation among gallery data can be also helpful for the person reidentification task. In this paper, we propose to investigate the high-order correlation among the probe and gallery data, not the pairwise matching, to jointly learn the relevance of gallery data to the probe. Recalling recent progresses on feature representation in person reidentification, it is difficult to select the best feature and each type of feature can benefit person description from different aspects. Under such circumstances, we propose a multihypergraph joint learning algorithm to learn the relevance in corporation with multiple features of the imaging data. More specifically, one hypergraph is constructed using one type of feature and multiple hypergraphs can be generated accordingly. Then, the learning process is conducted on the multihypergraph structure, and the identity of a probe is determined by its relevance to each gallery data. The merit of the proposed scheme is twofold. First, different from pairwise image matching, the proposed method jointly explores the relationships among different images. Second, multimodal data, i.e., different features, can be formulated in the multihypergraph structure, which can convey more information in the learning process and can be easily extended. We note that the proposed method is a general framework to incorporate with any combination of features, and thus is flexible in practice. Experimental results and comparisons with the state-of-the-art methods on three public benchmarking data sets demonstrate the superiority of the proposed method.

Correntropy-Induced Robust Low-Rank Hypergraph.

Hypergraph learning has been widely exploited in various image processing applications, due to its advantages in modeling the high-order information. Its efficacy highly depends on building an informative hypergraph structure to accurately and robustly formulate the underlying data correlation. However, the existing hypergraph learning methods are sensitive to non- Gaussian noise, which hurts the corresponding performance. In this paper, we present a noise-resistant hypergraph learning model, which provides superior robustness against various non- Gaussian noises. In particular, our model adopts low-rank representation to construct a hypergraph, which captures the globally linear data structure as well as preserving the grouping effect of highly-correlated data. We further introduce a correntropyinduced local metric to measure the reconstruction errors, which is particularly robust to non-Gaussian noises. Finally, the Frobenious-norm based regularization is proposed to combine with the low-rank regularizer, which enables our model to regularize the singular values of the coefficient matrix. By such, the non-zero coefficients are selected to generate a hyperedge set as well as the hyperedge weights. We have evaluated the proposed hypergraph model in the tasks of image clustering and semi-supervised image classification. Quantitatively, our scheme significantly enhances the performance of the state-of-the-art hypergraph models on several benchmark datasets.

Genetic variation in mitochondrial cox2 of Heterakis gallinarum from poultry in Sichuan, China.

Heterakis gallinarum is one of the common parasitic nematodes found in the caecum of poultry. To investigate the genetic diversity and genetic structure of the H. gallinarum population in Sichuan, we amplified and sequenced the complete mitochondrial (mt) cytochrome c oxidase subunit II (cox2) gene of 59 H. gallinarum isolates from seven different geographical regions, then analyzed their genetic polymorphisms. All cox2 genes of the 59 H. gallinarum isolates were 696 bp in length, with an average A + T content of 67.1%. Fifty-nine sequences contained 34 variable sites, and were classified into 23 haplotypes (HS1-HS23). The values of haplotype diversity (Hd) and nucleotide diversity (π) were 0.688 and 0.00288, respectively. Based on values of FST and Nm (FST = 0.01929, Nm = 12.71), there was a frequent gene flow but no significant genetic differentiation observed among the populations. The network map showed that the most prominent haplotype was HS1, and the other haplotypes (HS2-HS23) were centered on HS1 with a star-like topology, indicating that H. gallinarum had previously experienced a population expansion. To our knowledge, this is the first research on the population genetics of H. gallinarum based on mitochondrial cox2.