Variational Autoencoder(VAE) combines the ideas of autoencoders and variational inference, introducing the concept of latent space and variational inference to endow autoencoders to generate new images. VAE typically assumes that data follows a Gaussian distribution, but real data may follow other distributions. This inconsistency between the assumption and the true distribution can affect the modeling and reconstruction capabilities of VAE, which makes it difficult for traditional models to accurately capture the true distribution. To address the aforementioned issues, we propose a Prior Distribution Guided Gaussian Mixture Variational Autoencoder(PDGM-VAE). Specifically, we construct a Gaussian Mixture Prior Learner (GMPL) to capture complex features of the data distribution, enabling the model to learn and obtain a Gaussian mixture distribution that is reasonable and close to the real data distributions, which is then used as the prior distribution in the network. Furthermore, we build a Semantic-Aware Module with Embedded Prior Distribution (SAMEPD), integrating data and label information to learn the distribution parameters, enabling the network to learn and utilize the semantic knowledge contained in the labels. During training, by approximating the posterior distribution to the prior distribution, we enhance the model’s modeling and reconstruction capabilities, improving the quality of generated images. We evaluated the image generation task on five public datasets, and based on the FID metric, our proposed method outperformed other VAE methods.

Choroidal neovascularization (CNV) classification is a fine-grained classification task. Accurate classification of CNV in optical coherence tomography (OCT) images is crucial for clinical treatment. However, image acquisition noise degrades image quality and exacerbates confirmation bias from class imbalance in medical datasets. Moreover, significant inter-class ambiguity in fine-grained categories can misclassify informative samples (e.g., hard samples or minority class samples) when generating pseudo-labels, leading to sub-optimal classifiers. To address these challenges, we propose a difficulty-aware pseudo-label correction network (DPLC-Net). Specifically, we designed a robust feature mining module using feature similarity loss to maintain consistency between generated adversarial and original samples, enabling noise-resistant feature learning. A difficulty-aware pseudo-label correction module mines and corrects potential noisy pseudo-labels to improve classification performance. Finally, to alleviate data bias and leverage all unlabeled samples, we integrated a hybrid consistency and pseudo-labeling module comprising adaptive weighted consistency loss (AWCL) and class-aware dynamic threshold strategy (CDTS). AWCL adaptively learns weights for unlabeled samples, effectively utilizing all unlabeled data through weighted consistency loss. CDTS dynamically adjusts confidence thresholds based on class distribution and model learning status, improving pseudo-label quantity and quality. Experiments on private and public OCT datasets demonstrate that our method outperforms state-of-the-art methods.

In clinical practice, obtaining a large amount of labeled CNV data is very difficult. Semi-supervised learning can effectively utilize a large amount of unlabeled CNV data. Since CNV has complex features such as blurred and unevenly distributed pixels on the edges, there are differences in the segmentation difficulty between pixels in the same image. Existing semi-supervised segmentation methods do not consider the segmentation difficulty of pixels, which will reduce the segmentation accuracy. To address this problem, we propose a dual difficulty-aware adaptive pseudo-label learning (D2APL) method for semi-supervised CNV segmentation. The proposed dual difficulty awareness includes segmentation difficulty perception of pixels in labeled and unlabeled data. For labeled data, we propose a classification confidence-guided difficulty perception method. For unlabeled data, we propose a model stability-guided difficulty perception method. Finally, we propose a difficulty-aware self-training method to dynamically adjust the threshold of pseudolabels according to the difficulty, thereby improving the utilization of difficult-to-segment pixels in unlabeled data. Experimental results show that our method outperforms the state-of-the-art method in CNV segmentation.

Medical image segmentation is pivotal in quantifying tissue volumes, facilitating diagnoses, and enabling other critical medical applications. However, accurately segmenting medical images can be challenging because the complex intensity distribution inherent in the data arises from the highly complex interaction of many latent factors (data heterogeneity). In this context, we propose a novel method called Distribution-aware Contrastive Learning for Robust Segmentation (DCL-Seg) to address the inconsistency in medical image segmentation. Based on the assumption of content separability, we use learnable parameters to construct positive samples with a potential structure invariance via contrastive learning. In this way, our method can mitigate the negative effects of data heterogeneity to separate overlapped class distribution and structural solid boundary. We are in one public dataset and two clinical datasets for Breast tumor and Retinal vessel segmentation, which have achieved excellent results and widely proved the superiority of our method.

This paper presents a novel Transformer architecture for zero-shot learning (ZSL), termed TransZSL, which can characterize hierarchical semantic-aware parts. It consists of an adaptive token refinement (ATR), a hierarchical token aggregation (HTA), and semantic-aware prototypes (SAP). Firstly, the ViT is used as the backbone that provides comprehensive local information without missing details. To address the different degrees of noise caused by large appearance variations, the ATR is proposed to highlight important tokens and suppress useless ones adaptively. However, due to the complex image structure, some important tokens may be incorrectly discarded. Therefore, a random perturbation is proposed to reactivate discarded tokens randomly, reducing the risk of missing discriminative information. Secondly, dataset descriptions contain both low- and high-level attributes. To this end, the HTA aggregates complementary hierarchical tokens from multiple ViT layers. Thirdly, semantically similar content may be distributed in different tokens. To overcome this issue, the SAP is proposed to group semantically identical tokens into one prototype, focusing on semantic-aware parts. Besides, diversity loss is used to encourage networks to learn diverse prototypes that discover diverse parts. Both qualitative and quantitative results on several challenging tasks demonstrate the usefulness and effectiveness of our proposed methods.

The emergence of anti-vascular endothelial growth factor (anti-VEGF) therapy has revolutionized neovascular age-related macular degeneration (nAMD). Post-therapeutic optical coherence tomography (OCT) imaging facilitates the prediction of therapeutic response to anti-VEGF therapy for nAMD. Although the generative adversarial network (GAN) is a popular generative model for post-therapeutic OCT image generation, it is realistically challenging to gather sufficient pre- and post-therapeutic OCT image pairs, resulting in overfitting. Moreover, the available GAN-based methods ignore local details, such as the biomarkers that are essential for nAMD treatment. To address these issues, a Biomarkers-aware Asymmetric Bibranch GAN (BAABGAN) is proposed to efficiently generate post-therapeutic OCT images. Specifically, one branch is developed to learn prior knowledge with a high degree of transferability from large-scale data, termed the source branch. Then, the source branch transfer knowledge to another branch, which is trained on small-scale paired data, termed the target branch. To boost the transferability, a novel Adaptive Memory Batch Normalization (AMBN) is introduced in the source branch, which learns more effective global knowledge that is impervious to noise via memory mechanism. Also, a novel Adaptive Biomarkers-aware Attention (ABA) module is proposed to encode biomarkers information into latent features of target branches to learn finer local details of biomarkers. The proposed method outperforms traditional GAN models and can produce high-quality post-treatment OCT pictures with limited data sets, as shown by the results of experiments.

Source free domain adaptation (SFDA) transfers a single-source model to the unlabeled target domain without accessing the source data. With the intelligence development of various fields, a zoo of source models is more commonly available, arising in a new setting called multi-source-free domain adaptation (MSFDA). We find that the critical inborn challenge of MSFDA is how to estimate the importance (contribution) of each source model. In this paper, we shed new Bayesian light on the fact that the posterior probability of source importance connects to discriminability and transferability. We propose Discriminability And Transferability Estimation (DATE), a universal solution for source importance estimation. Specifically, a proxy discriminability perception module equips with habitat uncertainty and density to evaluate each sample's surrounding environment. A source-similarity transferability perception module quantifies the data distribution similarity and encourages the transferability to be reasonably distributed with a domain diversity loss. Extensive experiments show that DATE can precisely and objectively estimate the source importance and outperform prior arts by non-trivial margins. Moreover, experiments demonstrate that DATE can take the most popular SFDA networks as backbones and make them become advanced MSFDA solutions.

Existing multi-view representation learning methods typically follow a specific-to-uniform pipeline, extracting latent features from each view and then fusing or aligning them to obtain the unified object representation. However, the manually pre-specified fusion functions and aligning criteria could potentially degrade the quality of the derived representation. To overcome them, we propose a novel uniform-to-specific multi-view learning framework from a meta-learning perspective, where the unified representation no longer involves manual manipulation but is automatically derived from a meta-learner named MetaViewer. Specifically, we formulated the extraction and fusion of view-specific latent features as a nested optimization problem and solved it by using a bi-level optimization scheme. In this way, MetaViewer automatically fuses view-specific features into a unified one and learns the optimal fusion scheme by observing reconstruction processes from the unified to the specific over all views. Extensive experimental results in downstream classification and clustering tasks demonstrate the efficiency and effectiveness of the proposed method.

Scene classification is one of the most commonly studied areas of parsing the earth observation data. How to effectively interpreting the remote sensing images and extracting informative features are the great challenges for remote sensing image classification. Many important applications, such as land management and urban analysis, are based on the performance of remote sensing classification model. Recently, a lot of CNN based methods have been proposed and achieve promising results. Inspired by the success of knowledge distillation which transfers the learned information from a teacher model to a student model, a knowledge distillation based framework is proposed in this paper to handle the task of remote sensing scene classification from coarse to fine. Specifically, the learned knowledge from the teacher network is transformed into the coarse soft label and fine output mask to better guiding the student network to learn more informative features. Experiments are conducted on two widely used remote sensing scene datasets to evaluate the effectiveness of the proposed method and achieve comparable results compared with some state-of-the-art methods.

Multi-view (representation) learning derives an entity's representation from its multiple observable views to facilitate various downstream tasks. The most challenging topic is how to model unobserved entities and their relationships to specific views. To this end, this work proposes a novel multi-view learning method using a View-Aware parameter Modulation mechanism, termed VAM. The key idea is to use trainable parameters as proxies for unobserved entities and views, such that modeling entity-view relationships is converted into modeling the relationship between proxy parameters. Specifically, we first build a set of trainable parameters to learn a mapping from multi-view data to the unified representation as the entity proxy. Then we learn a prototype for each view and design a Modulation Parameter Generator (MPG) that learns a set of view-aware scale and shift parameters from prototypes to modulate the entity proxy and obtain view proxies. By constraining the representativeness, uniqueness, and simplicity of the proxies and proposing an entity-view contrastive loss, parameters are alternatively updated. We end up with a set of discriminative prototypes, view proxies, and an entity proxy that are flexible enough to yield robust representations for out-of-sample entities. Extensive experiments on five datasets show that the results of our VAM outperform existing methods in both classification and clustering tasks.

Choroidal neovascularization (CNV) is one of the severe eye disease. The severe results will cause of loss of acuity, scotomata, and distortion of vision. Automatic and accurate classification of CNV with optical coherence tomography (OCT) images can assist doctors in treatment. However, the existing methods ignore the fact that semantic feature maps, used for classification, lose much feature detail information. Therefore, we proposed a local detail enhancement network for CNV classification, which includes both progressive training mode and local detail enhancement (LDE) module. With the progressive training mode, the learned features fuse shallow and stable fine-grained information with high-level semantic information, which promote the diversity of the learned features. In LDE module, the detail feature learn (DFL) module is introduced to learn the underlying detail information and embed it into the semantic feature map. The semantic feature map with detail information is propitious to capture the subtle discrepancy between different CNV types and promote the classification performance. Sufficient experiments are performed on our self-build CNV dataset. Our method excelled existing methods and in evaluation indicators ACC, AUC, SEN, and SPE are 92.3%, 87.1%, 91.5%, and 90.9%.

Benefiting from the development of medical imaging, the automatic breast image classification has been extensively studied in a variety of breast cancer diagnosis tasks recently. The multi-modality image fusion was helpful to further improve classification performance. However, existing multi-modality fusion methods focused on the fusion of modalities, ignoring the interactions between modalities, which caused the inefficient performance. To address the above issues, we proposed a novel attention-based interactions network for breast tumor classification by using diffusion-weighted imaging (DWI) and apparent dispersion coefficient (ADC) images. Specifically, we proposed a multi-modality interaction mechanism, including relational interaction, channel interaction, and discriminative interaction, to design an attention-based interaction module, which enhanced the abilities of inter-modal interactions. Extensive ablation studies have been carried out, which provably affirmed the advantages of each component. The area under the receiver operating characteristic curve (AUC), accuracy (ACC), specificity (SPC), and sensitivity (SEN) were 87.0%, 87.0%, 88.0%, and 86.0%, respectively, also verifying its effectiveness.

In clinical practice, as a powerful and independent risk indicator of cardiovascular disease (CVD), accurate coronary artery calcium (CAC) segmentation can provide important information for the early diagnosis of CVD. However, due to the small and inconsistent CAC usually has fuzzy boundaries, which leads existing segmentation methods to suffer from unsatisfactory performance. To tackle this challenge, we propose a novel Efficient Multi-scale Vision Transformers for CAC segmentation (CAC-EMVT), which uses both the local and global branches to jointly model short- and long-range dependencies. CAC-EMVT is mainly composed of three modules: 1) a key factor sampling (KFS) module, which is used to mine the key factors of the image to perform low-rank reconstruction of highly structured features; 2) a non-local sparse context fusion (NSCF) module, which is used to efficiently model the global context information of shallow texture features; and 3) a non-local multi-scale context aggregation (NMCA) module, which can be applied to cross-level features to collect long-range dependencies from multiple scales. Undeniably, the newly proposed decomposable positional encoding plays a vital role in the performance improvement of the above modules. Extensive experiments are conducted on the CT scans of 130 CVD patients under 4-fold cross-validation and have demonstrated our CAC-EMVT notably outperforms the state-of-the-art methods in terms of both the mean Dice similarity coefficient (mDice) of 75.39%± 3.17 and mean surface distance (MSD) of 1.93%± 0.46. This reveals the effectiveness and the potential of our model in the clinical setting.

Cross-modal hashing is for the purpose of compressing heterogeneous multi-modal data into compact binary codes for the cross-modal retrieval, where accuracy and efficiency are two primary issues. To achieve high accuracy and efficiency, we put forward a novel method named Fast Unmediated Hashing (FUH) for cross-modal retrieval. For this method, motivated by the fact that label vector is a natural binary representation of samples for retrieval, we directly learn the cross-modal hash codes from semantic labels without any intermediate representation. This will capture more relations among different modalities, and reduce the number of variables. However, directly learning hash codes from labels would weaken the discrimination of hash codes. To address this issue, double supervision involving label information and pairwise similarity is proposed to enhance the discrimination. In addition, to decrease the training time, we present a strategy to bypass the similarity matrix-related operation in each iteration of optimization, thus some other related terms can also be computed offline to lower training complexity. Compared to several state-of-the-art techniques on three public datasets, the experimental results have manifested the superiority of FUH concerning efficiency and accuracy.

In second paragraph of the footnote on the first page of[1], the institution information of Lu Yang and Xiaoming Xi is inaccurate. The correct institution name is “School of Computer Science and Technology, Shandong University of Finance and Economics.” So this paragraph should be corrected as:

Vehicle re-identification (re-ID) aims to retrieve the image of the same vehicles across multiple cameras. It has attracted wide attention in the field of computer vision owing to the deployment of surveillance system. However, some unfavorable factors restrict the retrieval accuracy of re-ID; minor inter-class difference and orientation variation are two main issues. In this study, we proposed a multi-branch network based on common field of view (CFVMNet) to address these issues. In the proposed method, we extracted and fused the global and local detail features using four branches and the Batch DropBlock (BDB) strategy to accentuate inter-class difference. We also considered some other attributes (i.e., color, type, and model) in the feature extraction process to make the final features more recognizable. For the issue of orientation variation that could lead to large intra-class difference, we learned two different metrics according to whether there is common field of view of two vehicle images, respectively, which can enable the proposed CFVMNet to focus on different regions. Extensive experiments on two public datasets, VeRi-776 and VehicleID, show that the proposed method outperformed the state-of-the-art approaches to vehicle re-ID.

Cloud security has become a vital issue within thousands of inter-connected servers in clouds, as malicious attacks or discovered vulnerabilities may spread more rapidly than ever. Based on the opinion that hardware is more secure and trustworthy, a trust platform module (TPM) is used as an external chip to ensure the trust verification, while it's unsuitable as virtual machine (VM) migration, hybrid servers, distributed storage with a low performance. So, we design a novel cloud architecture with a special physical server named as the trust verification server (TVS) to provide trust services according to the TPM specification, then the servers in the cloud can use TVS remotely as a high-performance TPM chip. In this paper, we design the TVS with accelerator hardware, upgrade the cloud architecture with an additional certificate authority (CA) server, and use TVS with a non-interference trust measurement model. The experiments show that the TVS can work efficiently with huge performance improvements at more than 100 times compared with the use of TPM in the cloud. This can be used to solve the complex cloud security problems such as VM sprawl and VM escape.

The linear model is commonly utilized in hashing methods owing to its efficiency. To obtain better accuracy, linear-based hashing methods focus on designing a generalized linear objective function with different constraints or penalty terms that consider neighborhood information. In this study, we propose a novel generalized framework called Model Boost (MoBoost), which can achieve the self-improvement of the linear-based hashing. The proposed MoBoost is used to improve model parameter optimization for linear-based hashing methods without adding new constraints or penalty terms. In the proposed MoBoost, given a linear-based hashing method, we first execute the method several times to get several different hash codes for training samples, and then combine these different hash codes into one set utilizing one novel fusion strategy. Based on this set of hash codes, we learn some new parameters for the linear hash function that can significantly improve accuracy. The proposed MoBoost can be generally adopted in existing linear-based hashing methods, achieving more precise and stable performance compared to the original methods while imposing negligible added expenditure in terms of time and space. Extensive experiments are performed based on three benchmark datasets, and the results demonstrate the superior performance of the proposed framework.

Hashing is widely used to solve fast Approximate Nearest Neighbor (ANN) search problems, involves converting the original real-valued samples to binary-valued representations. The conventional quantization strategies, such as Single-Bit Quantization and Multi-Bit quantization, are considered ineffective, because of their serious information loss. To address this issue, we propose a novel variable-length quantization (VLQ) strategy for hashing. In the proposed VLQ technique, we divide all samples into different regions in each dimension firstly given the real-valued features of samples. Then we compute the dispersion degrees of these regions. Subsequently, we attempt to optimally assign different number of bits to each dimensions to obtain the minimum dispersion degree. Our experiments show that the VLQ strategy achieves not only superior performance over the state-of-the-art methods, but also has a faster retrieval speed on public datasets.

Hashing can compress high-dimensional data into compact binary codes, while preserving the similarity, to facilitate efficient retrieval and storage. However, when retrieving using an extremely short length hash code learned by the existing methods, the performance cannot be guaranteed because of severe information loss. To address this issue, in this study, we propose a novel supervised short-length hashing (SSLH). In this proposed SSLH, mutual reconstruction between the short-length hash codes and original features are performed to reduce semantic loss. Furthermore, to enhance the robustness and accuracy of the hash representation, a robust estimator term is added to fully utilize the label information. Extensive experiments conducted on four image benchmarks demonstrate the superior performance of the proposed SSLH with short-length hash codes. In addition, the proposed SSLH outperforms the existing methods, with long-length hash codes. To the best of our knowledge, this is the first linear-based hashing method that focuses on both short and long-length hash codes for maintaining high precision.

Vein pattern-based methods powerfully boost the recognition accuracy of finger veins, but real-time recognition cannot be guaranteed, especially in large-scale applications. Moreover, previous studies focused on either the matching task to enhance the accuracy or the indexing task to improve the efficiency. This paper proposes a finger vein code indexing method and combines it with a finger vein pattern matching method into an integration framework for improving both accuracy and efficiency. With the extracted vein patterns, the direction of each vein segment is detected and represented by the elliptical direction map as a feature for indexing, which will be encoded into a binary code by the angle K-means. The similarity between vein direction codes is measured by the grouped hamming distance in indexing, and further weighted by the overlap degree of the corresponding vein patterns to return the candidates for the probe. In addition, based on the above distance measurement, only vein segments with the same direction code are considered in following probe-to-candidate matching. Experimental results indicate that our indexing method outperforms the state-of-the-art methods and has competitive potential in performing the matching task. The results also indicate that the integration framework highly improves the identification efficiency with a slight improvement on the accuracy.

Retinal layer segmentation in optical coherence tomography (OCT) images is crucial for the diagnosis and study of retinal diseases. Graph-based methods are commonly used in layer segmentation. However, most of these methods require a lot of human efforts for determining an appropriate model to compute good edge weights. In this paper, we propose a novel automatic method for segmenting retinal layers in macular OCT images. Specially, we propose a new fully convolutional deep learning architecture with a side output layer to directly learn optimal graph-edge weights from raw pixels. The architecture can automatically learn multi-scale and multi-level features to generate accurate boundary probabilities as good edge weights without hand-crafted appropriate models. The boundaries are finalized by using graph segmentation method. The proposed method is evaluated on a dataset with 130 OCT B-scans. The experimental results show the mean absolute boundary positioning differences are 1.48±0.34 pixel.

Finger vein recognition has received a lot of attention recently and is viewed as a promising biometric trait. In related methods, vein pattern-based methods explore intrinsic finger vein recognition, but their performance remains unsatisfactory owing to defective vein networks and weak matching. One important reason may be the neglect of deep analysis of the vein anatomy structure. By comprehensively exploring the anatomy structure and imaging characteristic of vein patterns, this paper proposes a novel finger vein recognition framework, including an anatomy structure analysis-based vein extraction algorithm and an integration matching strategy. Specifically, the vein pattern is extracted from the orientation map-guided curvature based on the valley- or half valley-shaped cross-sectional profile. In addition, the extracted vein pattern is further thinned and refined to obtain a reliable vein network. In addition to the vein network, the relatively clear vein branches in the image are mined from the vein pattern, referred to as the vein backbone. In matching, the vein backbone is used in vein network calibration to overcome finger displacements. The similarity of two calibrated vein networks is measured by the proposed elastic matching and further recomputed by integrating the overlap degree of corresponding vein backbones. Extensive experiments on two public finger vein databases verify the effectiveness of the proposed framework.

Local feature points have been widely employed in robust image fingerprinting. One of their intrinsic advantages is their invariance under geometric transforms. However, their robustness against certain attacks that modify the positions of points, such as additive noising and blurring, is limited. In addition, local-feature-point-based approaches ignore the distribution of the feature points. In this paper, we harness feature point relationships, including local structures and global relevance, to overcome these limitations. In the relationship mining strategy, Delaunay triangulation is first applied to the feature points to capture their geometric structures. Subsequently, local structures are represented by searching for an independent set in the mapping graph constructed via Delaunay triangulation, whereas the global relevance is represented by the Laplacian of the graph. Finally, the local structures and global relevance are used as input to the quantization process of the image fingerprinting system. In the process of quantization, we propose an unsupervised quantization strategy called between-cluster distance-based quantization to preserve the neighborhood structure between the binary fingerprint space and the original feature space. Experimental results show that the proposed method achieves effective performance under common modifications.

Nature Language Processing has been paid more attention recently. Traditional approaches for language model primarily rely on elaborately designed features and complicated natural language processing tools, which take a large amount of human effort and are prone to error propagation and data sparse problem. Deep neural network method has been shown to be able to learn implicit semantics of text without extra knowledge. To better learn deep underlying semantics of sentences, most deepneuralnetworklanguagemodelsutilizemulti-gramstrategy. However, the current multi-gram strategies in CNN framework are mostly realized by concatenating trained multi-gram vectors to form the sentence vector, which can increase the number of parameters to be learned and is prone to over fitting. To alleviate the problem mentioned above, we propose a novel adaptive sentence representation learning model based on multigram CNN framework. It learns adaptive importance weights of different n-gram features and forms sentence representation by using weighted sum operation on extracted n-gram features, which can largely reduce parameters to be learned and alleviate the threat of over fitting. Experimental results show that the proposed method can improve performances when be used in sentiment and relation classification tasks.

Finger vein pattern has become one of the most promising biometric identifiers. In this paper, a robust method based on Bag-of-Words (BoW) is developed for finger vein verification. Firstly, some robust and discriminative visual words are learned from local base features such as Local Binary Pattern (LBP), Mean Curvature and Webber Local Descriptor (WLD). We name these visual words as Finger Vein Textons (FVTs). Secondly, each image is mapped into a FVTs matrix. Finally, spatial pyramid matching (SPM) method is applied to maintain spatial layout information by representing each image as pyramid histogram which is performed for matching by histogram intersection function. Experimental results show that the proposed method achieves satisfactory performance both on our database and the open PolyU database. In addition, our method also has strong robustness and high accuracy on the self-built rotation and illumination databases.

Finger vein pattern has become one of the most promising biometric identifiers. In this paper, we propose a robust finger vein verification method based on a personalized best patches map (PBPM). Firstly, some robust and discriminative visual words of finger vein are learned from traditional base feature such as local binary pattern (LBP). These visual words are named as finger vein textons (FVTs), which can well represent the visual primitives of finger vein. Secondly, we represent the finger vein image as a finger vein textons map (FVTM) by mapping each patch of the image into the closest FVT. Thirdly, by rejecting inconsistent patches, the PBPM of a certain individual is learned from these FVTMs which are extracted from the training samples of the same finger. Finally, the matched best patch ratio is used to measure similarity between the extracted FVTM of the input finger and the PBPM of a certain individual. Experimental results show that our method achieves satisfactory performance on the open PolyU database. In addition, it also has strong robustness and high accuracy on the self-built rotation and translation databases.

Finger veins based biometrics, as a new approach to personal identification, has received much attention in recent years. The methods based on low level feature, for instance the gray, texture of finger vein, are the mainstream, but they are usually faced with many challenges, such as sensitivity to noise and low local consistency. In fact, finger vein recognition based on high level feature representation has been proved to be a promising way to effectively overcome the above limitations and improve the system performance. Thus, in this paper, we present a novel identification framework, which utilizes superpixel-based features (SPFs) of finger vein for high level feature representation. When comparing two finger veins, the features of each pixel are firstly extracted as base attributes by traditional way. Then, after superpixel over-segmentation, the SPF of each finger vein can be obtained based on its base attributes by some statistical techniques. Lastly, a weighted spatial pyramid matching (WSPM) scheme is utilized to implement matching. Our experiments have yielded some very good results evidenced by an EER of 0.0147 on the benchmark database PolyU.

Importance weighted active learning (IWAL) introduces a weighting scheme to measure the importance of each instance for correcting the sampling bias of the probability distributions between training and test datasets. However, the weighting scheme of IWAL involves the distribution of the test data, which can be straightforwardly estimated in active learning by interactively querying users for labels of selected test instances, but difficult for conventional learning where there are no interactions with users, referred as passive learning. In this paper, we investigate the insufficient sampling bias problem, i.e., bias occurs only because of insufficient samples, but the sampling process is unbiased. In doing this, we present two assumptions on the sampling bias, based on which we propose a practical weighting scheme for the empirical loss function in conventional passive learning, and present IWPL, an importance weighted passive learning framework. Furthermore, we provide IWSVM, an importance weighted SVM for validation. Extensive experiments demonstrate significant advantages of IWSVM on benchmarks and synthetic datasets.

We evaluate two types of prosodic features utilizing automatically generated stress and tone labels for non-native read speech in terms of their applicability for automated speech scoring. Both types of features have not been used in the context of automated scoring of non-native read speech to date. In our first experiment, we compute features based on a positional match between automatically identified stress and tone labels for 741 non-native read text passages with a human gold standard on the same texts read by a native speaker. Pearson correlations of up to r=0.54 between these features and human proficiency scores are observed. In our second experiment, we use stress and tone labels of the same non-native read speech corpus to compute derived features of rhythm and relative frequencies, which then again are correlated with human proficiency scores. Pearson correlations of up to r=-0.38 are observed.