Weihua Ou's research while affiliated with Guizhou Normal University and other places

Publications (76)

Article
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In recent years, the physiological measurement based on remote photoplethysmography has attracted wide attention, especially since the epidemic of COVID-19. Many researchers paid great efforts to improve the robustness of illumination and motion variation. Most of the existing methods divided the ROIs into many sub-regions and extracted the heart r...
Article
Cross-modal hashing that leverages hash functions to project high-dimensional data from different modalities into the compact common hamming space, has shown immeasurable potential in cross-modal retrieval. To ease labor costs, unsupervised cross-modal hashing methods are proposed. However, existing unsupervised methods still suffer from two factor...
Preprint
Hashing that projects data into binary codes has shown extraordinary talents in cross-modal retrieval due to its low storage usage and high query speed. Despite their empirical success on some scenarios, existing cross-modal hashing methods usually fail to cross modality gap when fully-paired data with plenty of labeled information is nonexistent....
Article
Semi-supervised cross-modal retrieval is an eclectic paradigm which learns common representations via exploiting underlying semantic information from both labeled and unlabeled data. Most existing methods ignore the rich semantic information of text data and are unable to fully utilize the text data in common representation learning. Moreover, they...
Article
Blending free annotation cost, low memory usage and high query speed into a unity, unsupervised deep hashing has shown extraordinary talents in image retrieval. In order to address the lack of semantic information in unsupervised scenario, related works leverage models pretrained on large-scale datasets (e.g., ImageNet) to the estimate semantic sim...
Article
Exploiting relationship among samples in cross-modal data plays a key role in the task of cross-modal retrieval, but most of existing methods only extract the correlation from pairwise samples and ignore the relations of unpaired samples. Some graph regularization methods proposed a reasonable paradigm to exploit the correlation from multiple sampl...
Article
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Medical cross-modal retrieval aims to retrieve semantically similar medical instances across different modalities, such as retrieving X-ray images using radiology reports or retrieving radiology reports using X-ray images. The main challenge for medical cross-modal retrieval are the semantic gap and the small visual differences between different ca...
Preprint
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Mapping X-ray images, radiology reports, and other medical data as binary codes in the common space, which can assist clinicians to retrieve pathology-related data from heterogeneous modalities (i.e., hashing-based cross-modal medical data retrieval), provides a new view to promot computeraided diagnosis. Nevertheless, there remains a barrier to bo...
Article
Cross-modal hashing methods have attracted considerable attention due to their low memory usage and high query speed in large-scale cross-modal retrieval. During the encoding process, there still remains two crucial bottlenecks: how to equip hash codes with cross-modal semantic information, and how to rapidly obtain hash codes. In this paper, we pr...
Article
Transfer learning is known to an effective method dealing with domain shift. When the same task is shared in different domains, it is usually called domain adaptation. The problem of distribution difference is inevitable in domain adaption works. The subspace method can transform the data into a new feature representation, which is helpful to reduc...
Article
Due to the numerous real-world applications of the crowd counting job, it has become a popular research topic. Modern crowd counting systems have a sophisticated structure and employ a filter on a big image size, making them difficult to use. Because these technologies are computationally intensive and difficult to implement in small surveillance s...
Article
Automatic ultrasound image segmentation plays an important role in early diagnosis of human diseases. This paper introduces a novel and efficient encoder–decoder network, called Lightweight Attention Encoder–Decoder Network (LAEDNet), for automatic ultrasound image segmentation. In contrast to previous encoder–decoder networks that involve complica...
Article
With the wide application of radiography, massive data of chest X-ray and the associated radiology reports have been accumulated. Cross-modal retrieval between the chest X-ray and radiology reports is useful for the level of practicing radiologists and substantial other medical settings. However, existing cross-modal retrieval methods, which are ma...
Article
Collaborative representation-based classification (CRC), as a typical kind of linear representation-based classification, has attracted more attention due to the effective and efficient pattern classification performance. However, the existing class-specific representations are not competitively learned from collaborative representation for achievi...
Article
K-nearest neighbor rule (KNN) has been regarded as one of the top 10 methods in the field of data mining. Due to its simplicity and effectiveness, it has been widely studied and applied to various classification tasks. In this article, we develop a novel representation coefficient-based k-nearest centroid neighbor method (RCKNCN), which aims to fur...
Article
Cross-modal hashing is an effective cross-modal retrieval approach because of its low storage and high efficiency. However, most existing methods mainly utilize pre-trained networks to extract modality-specific features, while ignore the position information and lack information interaction between different modalities. To address those problems, i...
Article
Recently, model compression has been widely used for the deployment of cumbersome deep models on resource-limited edge devices in the performance-demanding industrial Internet of Things (IoT) scenarios. As a simple yet effective model compression technique, knowledge distillation (KD) aims to transfer the knowledge (e.g., sample relationships as th...
Article
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Existing methods for human pose estimation usually use a large intermediate tensor, leading to a high computational load, which is detrimental to resource-limited devices. To solve this problem, we propose a low computational cost pose estimation network, MobilePoseNet, which includes encoder, decoder, and parallel nonmaximum suppression operation....
Article
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How to represent and classify a testing sample for the representation-based classification (RBC) plays an important role in the filed of pattern recognition. As a typical kind of the representation-based classification with promising performance, collaborative representation-based classification (CRC) adopts all the training samples to collaborativ...
Article
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Deep neural networks have achieved a great success in a variety of applications, such as self-driving cars and intelligent robotics. Meanwhile, knowledge distillation has received increasing attention as an effective model compression technique for training very efficient deep models. The performance of the student network obtained through knowledg...
Article
Many methods based on matrix factorization have recently been proposed and achieve good performance in many practical applications. Latent low-rank representation (LatLRR) is a marvelous feature extraction method, and it has shown a powerful ability in extracting robust data features. However, LatLRR and the variants of LRR have some shortcomings a...
Article
Feature representation is highly important for many computer vision tasks. A broad range of prior studies have been proposed to strengthen representation ability of architectures via built-in blocks. However, during the forward propagation, the reduction in feature map scales still leads to the lack of representation ability. In this paper, we focu...
Article
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The huge computational overhead limits the inference of convolutional neural networks on mobile devices for object detection, which plays a critical role in many real-world scenes, such as face identification, autonomous driving, and video surveillance. To solve this problem, this paper introduces a lightweight convolutional neural network, called...
Article
The traditional manifold learning methods usually utilize the original observed data to directly define the intrinsic structure among data. Because the original samples often contain a deal of redundant information or it is corrupted by noises, it leads to the unreliability of the obtained intrinsic structure. In addition, the intrinsic structure l...
Article
In this paper, we proposed a semi-supervised common representation learning method with GAN-based Asymmetric Transfer Network (GATN) for cross modality retrieval. GATN utilizes the asymmetric pipeline to guarantee the semantic consistency and adopt (Generative Adversarial Network) GAN to fit the distributions of different modalities. Specifically,...
Article
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Collaborative representation-based classification (CRC) is one of the famous representation-based classification methods in pattern recognition. However, a testing sample in most of the CRC variants is collaboratively reconstructed by a linear combination of all the training samples from all the classes, the training samples from the class that the...
Article
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Cross-modal retrieval has been attracted attentively in the past years. Recently, the collective matrix factorization was proposed to learn the common representations for cross-modal retrieval based on assumption that the pairwise data from different modalities should have the same common semantic representations. However, this unified common repre...
Article
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Cross-modal retrieval aims to search the semantically similar instances from the other modalities by giving a query from one modality. Recently, generative adversarial networks (GANs) has been proposed to model the joint distribution over the data from different modalities and to learn the common representations for cross-modal retrieval. However,...
Article
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Visual saliency detection plays a significant role in the fields of computer vision. In this paper, we introduce a novel saliency detection method based on weighted linear multiple kernel learning (WLMKL) framework, which is able to adaptively combine different contrast measurements in a supervised manner. As most influential factor is contrast ope...
Article
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Question answer selection in the Chinese medical field is very challenging since it requires effective text representations to capture the complex semantic relationships between Chinese questions and answers. Recent approaches on deep learning, e.g., CNN and RNN, have shown their potential in improving the selection quality. However, these existing...
Article
Representation-based classification (RBC) has attracted much attention in pattern recognition. As a linear representative RBC method, collaborative representation-based classification (CRC) is very promising for classification. Although many extensions of CRC have been developed recently, the discriminative and competitive representations of differ...
Article
Full-text available
Cross-modal retrieval aims to search the semantically similar instances from the other modalities given a query from one modality. However, the differences of the distributions and representations between different modalities make that the similarity of different modalities can not be measured directly. To address this problem, in this paper, we pr...
Article
Graph-based feature extraction is an efficient technique for data dimensionality reduction, and it has gained intensive attention in various fields such as image processing, pattern recognition, and machine learning. However, conventional graph-based dimensionality reduction algorithms usually depend on a fixed weight graph called similarity matrix...
Article
Collaborative representation-based classification (CRC) is a famous representation-based classification method in pattern recognition. Recently, many variants of CRC have been designed for many classification tasks with the good classification performance. However, most of them ignore the inter-class pattern discrimination among the class-specific...
Article
In this paper, a dual switching discrete‐time linear system, simultaneously subject to deterministic switching and Markov chain, is considered. This study does not consider the transition probability of the Markov chain as fixed but determined by the current position of deterministic switching. Namely, such dual switching discrete‐time linear syste...
Chapter
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Question and answer matching in Chinese medical science is a challenging problem, which requires an effective text semantic representation. In recent years, deep learning has achieved brilliant achievements in natural language processing field, which is utilized to capture various semantic features. In this paper, we propose a neural network, i.e.,...
Chapter
Cross-modal retrieval returns the relevant results from the other modalities given a query from one modality. The main challenge of cross-modal retrieval is the “heterogeneity gap” amongst modalities, because different modalities have different distributions and representations. Therefore, the similarity of different modalities can not be measured...
Chapter
In this paper, we present a novel framework to incorporate top-down guidance to identify salient objects. The salient regions/objects are predicted by transferring objectness prior without the requirement of center-biased assumption. The proposed framework consists of the following two basic steps: In the top-down process, we create a location sali...
Article
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Recently, collaborative representation-based classification (CRC) and its many variations have been widely applied for various classification tasks in pattern recognition. To further enhance the pattern discrimination of CRC, in this article we propose a novel extension of CRC, entitled discriminative, competitive, and collaborative representation-...
Article
Representation-based classification (RBC) methods have recently been the promising pattern recognition technologies for object recognition. The representation coefficients of RBC as the linear reconstruction measure (LRM) can be well used for classifying objects. In this article, we propose two enhanced linear reconstruction measure-based classific...
Article
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Recent years, many scientists address the research on text sentiment analysis of social media due to the exponential growth of social multimedia content. Natural language ambiguities and indirect sentiments within the social media text have made it hard to classify by using traditional machine learning approaches, such as support vector machines, n...
Article
Feature extraction plays an important role in the pattern recognition and computer vision. Existing methods only consider either the global structure or consider the local structure, while cannot capture both of them. To address this problem, in this paper, we propose a novel feature extraction algorithm called discriminative feature extraction bas...
Article
Multiview nonnegative matrix has shown many promising applications in computer vision and pattern recognition. However, most existing works focus on the view consistency and ignore the discrimination. In this paper, we introduce a novel discriminative multiview nonnegative matrix (DMultiNMF) algorithm to learn discriminative and consistent represen...
Article
K-nearest neighbor rule (KNN) is one of the most widely used methods in pattern recognition. However, the KNN-based classification performance is severely affected by the sensitivity of the neighborhood size k and the simple majority voting in the regions of k-neighborhoods, especially in the case of the small sample size with the existing outliers...
Article
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Recent years have witnessed the great progress for semantic segmentation using deep convolutional neural networks (DCNNs). This paper presents a novel fully convolutional network for semantic segmentation using multi-scale contextual convolutional features. Since objects in natural images tend to be with various scales and aspect ratios, capturing...
Article
Collaborative representation (CR) is one of the well-known representation methods and has been widely used in computer vision and pattern recognition. The collaborative representation-based classification (CRC) and its extension called the probabilistic collaborative representation-based classification (PCRC) have obtained promising performance in...
Article
For the hiding capacity and image quality of reversible data hiding algorithm based on image interpolation, a high capacity image reversible data hiding algorithm using interpolation and sorting is proposed. Firstly, in order to enhance the quality of the interpolated image, an improved image interpolation method was proposed. Then, the interpolate...
Article
K-nearest neighbor (KNN) rule is a well-known non-parametric classifier that is widely used in pattern recognition. However, the sensitivity of the neighborhood size k always seriously degrades the KNN-based classification performance, especially in the case of the small sample size with the existing outliers. To overcome this issue, in this articl...
Article
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Discriminative methods have been widely applied to construct the appearance model for visual tracking. Most existing methods incorporate online updating strategy to adapt to the appearance variations of targets. The focus of online updating for discriminative methods is to select the positive samples emerged in past frames to represent the appearan...
Article
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Face recognition plays a significant role in computer vision. It is well know that facial images are complex stimuli signals that suffer from non-rigid deformations, including misalignment, orientation, pose changes, and variations of facial expression, etc. In order to address these variations, this paper introduces an improved sparse-representati...
Article
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In the most tracking approaches, a score function is utilized to determine which candidate is the optimal one by measuring the similarity between the candidate and the template. However, the representative samples selection in the template update is challenging. To address this problem, in this paper, we treat the template as a linear combination o...
Article
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With the increasing availability of multiview nonnegative data in real applications, multiview representation learning based on nonnegative matrix factorization (NMF) has attracted more and more attentions. However, existing NMF-based methods are sensitive to noises and are difficult to generate discriminative features with noisy views. To address...
Conference Paper
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Recent years have witnessed the great progress for semantic segmentation using deep convolutional neural networks (DCNNs). This paper presents a novel fully convolutional network for semantic segmentation using multi-scale contextual convolutional features. Since objects in natural images tend to be with various scales and aspect ratios, capturing...
Article
Full-text available
In the past years, discriminative methods are popular in visual tracking. The main idea of the discriminative method is to learn a classifier to distinguish the target from the background. The key step is the update of the classifier. Usually, the tracked results are chosen as the positive samples to update the classifier, which results in the fail...
Article
Face recognition in real-world video surveillance needs to deal with a lot of challenges including low resolution, illumination variations, pose changes, occlusions and so on. Among them, occlusions are difficult and have not attracted enough attentions. To address this problem, in this paper, we propose a robust discriminative nonnegative dictiona...