ISSN:2579-0110(print)
ISSN:2579-0129(online)
Publication Frequency:Continuously
Journal of New Media (JNM) aims to provide a high quality and timely forum for researchers, engineers whose research interests focus on digital multimedia processing to share their state-of-the-art achievements, to learn the multimedia processing developments.
Starting from July 2023, Journal of New Media will transition to a continuous publication model, accepted articles will be promptly published online upon completion of the peer review and production processes.
Open Access
ARTICLE
Journal of New Media, Vol.6, pp. 1-16, 2024, DOI:10.32604/jnm.2024.045833
Abstract With the development of cities and the prevalence of networks, interpersonal relationships have become increasingly distant. When people crave communication, they hope to find someone to confide in. With the rapid advancement of deep learning and big data technologies, an enabling environment has been established for the development of intelligent chatbot systems. By effectively combining cutting-edge technologies with human-centered design principles, chatbots hold the potential to revolutionize our lives and alleviate feelings of loneliness. A multi-topic chat companion robot based on a state machine has been proposed, which can engage in fluent dialogue with humans and meet different functional requirements.… More >
Open Access
ARTICLE
Journal of New Media, Vol.4, No.1, pp. 1-11, 2022, DOI:10.32604/jnm.2022.027054
Abstract With the improvement of people's security awareness, numerous monitoring equipment has been put into use, resulting in the explosive growth of surveillance video data. Key frame extraction technology is a paramount technology for improving video storage efficiency and enhancing the accuracy of video retrieval. It can extract key frame sets that can express video content from massive videos. However, the existing key frame extraction algorithms of surveillance video still have deficiencies, such as the destruction of image information integrity and the inability to extract key frames accurately. To this end, this paper proposes a key frame extraction algorithm of surveillance… More >
Open Access
ARTICLE
Journal of New Media, Vol.4, No.1, pp. 41-50, 2022, DOI:10.32604/jnm.2022.027883
Abstract Aiming at the safety problems of toxic, flammable and explosive chemicals used in middle school chemical experiments, such as human poisoning, skin corrosion, fire or explosion caused by improper experimental operation, a virtual simulation method of chemical experiments based on unity is proposed. Due to the need to analyze and compare the data in chemical experiments, summarize the experimental characteristics and data relevance. Therefore, based on the Apriori algorithm, this method deeply excavates the data obtained in the chemical experiment, uses Maya to model the experimental environment, uses unity to design the interactive functions in the experimental process, and uses… More >
Open Access
ARTICLE
Journal of New Media, Vol.4, No.1, pp. 13-26, 2022, DOI:10.32604/jnm.2022.027312
Abstract With the speedy development of communication Internet and the widespread use of social multimedia, so many creators have published posts on social multimedia platforms that fake news detection has already been a challenging task. Although some works use deep learning methods to capture visual and textual information of posts, most existing methods cannot explicitly model the binary relations among image regions or text tokens to mine the global relation information in a modality deeply such as image or text. Moreover, they cannot fully exploit the supplementary cross-modal information, including image and text relations, to supplement and enrich each modality. In… More >
Open Access
ARTICLE
Journal of New Media, Vol.4, No.3, pp. 117-123, 2022, DOI:10.32604/jnm.2022.027060
Abstract The Hessian matrix has a wide range of applications in image processing, such as edge detection, feature point detection, etc. This paper proposes an image enhancement algorithm based on the Hessian matrix. First, the Hessian matrix is obtained by convolving the derivative of the Gaussian function. Then use the Hessian matrix to enhance the linear structure in the image. Experimental results show that the method proposed in this paper has strong robustness and accuracy. More >
Open Access
ARTICLE
Journal of New Media, Vol.4, No.1, pp. 51-58, 2022, DOI:10.32604/jnm.2022.028775
Abstract With the development of new media technology, vehicle matching plays a further significant role in video surveillance systems. Recent methods explored the vehicle matching based on the feature extraction. Meanwhile, similarity metric learning also has achieved enormous progress in vehicle matching. But most of these methods are less effective in some realistic scenarios where vehicles usually be captured in different times. To address this cross-domain problem, we propose a cross-domain similarity metric learning method that utilizes the GAN to generate vehicle images with another domain and propose the two-channel Siamese network to learn a similarity metric from both domains (i.e.,… More >
Open Access
ARTICLE
Journal of New Media, Vol.4, No.3, pp. 125-135, 2022, DOI:10.32604/jnm.2022.027199
Abstract In order to apply the deep learning to the stereo image quality evaluation, two problems need to be solved: The first one is that we have a bit of training samples, another is how to input the dimensional image’s left view or right view. In this paper, we transfer the 2D image quality evaluation model to the stereo image quality evaluation, and this method solves the first problem; use the method of principal component analysis is used to fuse the left and right views into an input image in order to solve the second problem. At the same time, the… More >
Open Access
ARTICLE
Journal of New Media, Vol.4, No.3, pp. 137-143, 2022, DOI:10.32604/jnm.2022.027890
Abstract Recent advances in OCR show that end-to-end (E2E) training pipelines including detection and identification can achieve the best results. However, many existing methods usually focus on case insensitive English characters. In this paper, we apply an E2E approach, the multiplex multilingual mask TextSpotter, which performs script recognition at the word level and uses different recognition headers to process different scripts while maintaining uniform loss, thus optimizing script recognition and multiple recognition headers simultaneously. Experiments show that this method is superior to the single-head model with similar number of parameters in end-to-end identification tasks. More >
Open Access
ARTICLE
Journal of New Media, Vol.4, No.1, pp. 27-39, 2022, DOI:10.32604/jnm.2022.027826
Abstract Research shows that deep learning algorithms can effectively improve a single image's super-resolution quality. However, if the algorithm is solely focused on increasing network depth and the desired result is not achieved, difficulties in the training process are more likely to arise. Simultaneously, the function space that can be transferred from a low-resolution image to a high-resolution image is enormous, making finding a satisfactory solution difficult. In this paper, we propose a deep learning method for single image super-resolution. The MDRN network framework uses multi-scale residual blocks and dual learning to fully acquire features in low-resolution images. Finally, these features… More >
Open Access
ARTICLE
Journal of New Media, Vol.4, No.3, pp. 155-164, 2022, DOI:10.32604/jnm.2022.031113
Abstract At present, segmentation for medical image is mainly based on fully supervised model training, which consumes a lot of time and labor for dataset labeling. To address this issue, we propose a semi-supervised medical image segmentation model based on a generative adversarial network framework for automated segmentation of arteries. The network is mainly composed of two parts: a segmentation network for medical image segmentation and a discriminant network for evaluating segmentation results. In the initial stage of network training, a fully supervised training method is adopted to make the segmentation network and the discrimination network have certain segmentation and discrimination… More >
Open Access
ARTICLE
Journal of New Media, Vol.4, No.3, pp. 145-153, 2022, DOI:10.32604/jnm.2022.030178
Abstract Microphone array-based sound source localization (SSL) is widely used in a variety of occasions such as video conferencing, robotic hearing, speech enhancement, speech recognition and so on. The traditional SSL methods cannot achieve satisfactory performance in adverse noisy and reverberant environments. In order to improve localization performance, a novel SSL algorithm using convolutional residual network (CRN) is proposed in this paper. The spatial features including time difference of arrivals (TDOAs) between microphone pairs and steered response power-phase transform (SRP-PHAT) spatial spectrum are extracted in each Gammatone sub-band. The spatial features of different sub-bands with a frame are combine into a… More >
Open Access
ARTICLE
Journal of New Media, Vol.1, No.2, pp. 63-74, 2019, DOI:10.32604/jnm.2019.06253
Abstract Video object tracking is an important research topic of computer vision, which finds a wide range of applications in video surveillance, robotics, human-computer interaction and so on. Although many moving object tracking algorithms have been proposed, there are still many difficulties in the actual tracking process, such as illumination change, occlusion, motion blurring, scale change, self-change and so on. Therefore, the development of object tracking technology is still challenging. The emergence of deep learning theory and method provides a new opportunity for the research of object tracking, and it is also the main theoretical framework for the research of moving… More >
Open Access
ARTICLE
Journal of New Media, Vol.1, No.1, pp. 1-9, 2019, DOI:10.32604/jnm.2019.05937
Abstract Clouds play an important role in modulating radiation processes and climate changes in the Earth's atmosphere. Currently, measurement of meteorological elements such as temperature, air pressure, humidity, and wind has been automated. However, the cloud's automatic identification technology is still not perfect. Thus, this paper presents an approach that extracts dense scale-invariant feature transform (Dense_SIFT) as the local features of four typical cloud images. The extracted cloud features are then clustered by K-means algorithm, and the bag-of-words (BoW) model is used to describe each ground-based cloud image. Finally, support vector machine (SVM) is used for classification and recognition. Based on… More >
Open Access
ARTICLE
Journal of New Media, Vol.1, No.2, pp. 75-85, 2019, DOI:10.32604/jnm.2019.06259
Abstract In order to improve the quality of low-dose computational tomography (CT) images, the paper proposes an improved image denoising approach based on WGAN-gp with Wasserstein distance. For improving the training and the convergence efficiency, the given method introduces the gradient penalty term to WGAN network. The novel perceptual loss is introduced to make the texture information of the low-dose images sensitive to the diagnostician eye. The experimental results show that compared with the state-of-art methods, the time complexity is reduced, and the visual quality of low-dose CT images is significantly improved. More >
Open Access
ARTICLE
Journal of New Media, Vol.1, No.1, pp. 11-25, 2019, DOI:10.32604/jnm.2019.06219
Abstract Digital images can be tampered easily with simple image editing software tools. Therefore, image forensic investigation on the authenticity of digital images’ content is increasingly important. Copy-move is one of the most common types of image forgeries. Thus, an overview of the traditional and the recent copy-move forgery localization methods using passive techniques is presented in this paper. These methods are classified into three types: block-based methods, keypoint-based methods, and deep learning-based methods. In addition, the strengths and weaknesses of these methods are compared and analyzed in robustness and computational cost. Finally, further research directions are discussed. More >
Open Access
ARTICLE
Journal of New Media, Vol.1, No.1, pp. 35-44, 2019, DOI:10.32604/jnm.2019.05803
Abstract Image restoration is an image processing technology with great practical value in the field of computer vision. It is a computer technology that estimates the image information of the damaged area according to the residual image information of the damaged image and carries out automatic repair. This article firstly classify and summarize image restoration algorithms, and describe recent advances in the research respectively from three aspects including image restoration based on partial differential equation, based on the texture of image restoration and based on deep learning, then make the brief analysis of digital image restoration of subjective and objective evaluation… More >
Open Access
ARTICLE
Journal of New Media, Vol.1, No.2, pp. 87-99, 2019, DOI:10.32604/jnm.2019.06582
Abstract Recently, image representations derived by convolutional neural networks (CNN) have achieved promising performance for instance retrieval, and they outperform the traditional hand-crafted image features. However, most of existing CNN-based features are proposed to describe the entire images, and thus they are less robust to background clutter. This paper proposes a region of interest (RoI)-based deep convolutional representation for instance retrieval. It first detects the region of interests (RoIs) from an image, and then extracts a set of RoI-based CNN features from the fully-connected layer of CNN. The proposed RoI-based CNN feature describes the patterns of the detected RoIs, so that… More >
Open Access
ARTICLE
Journal of New Media, Vol.2, No.3, pp. 99-109, 2020, DOI:10.32604/jnm.2020.09889
Abstract This paper analyzes the energy consumption situation in Beijing, based
on the comparison of common energy consumption prediction methods. Here we
use multiple linear regression analysis, grey prediction, BP neural net-work
prediction, grey BP neural network prediction combined method, LSTM long-term
and short-term memory network model prediction method. Firstly, before
constructing the model, the whole model is explained theoretically. The advantages
and disadvantages of each model are analyzed before the modeling, and the
corresponding advantages and disadvantages of these models are pointed out.
Finally, these models are used to construct the Beijing energy forecasting model, and
some years are selected… More >
Open Access
ARTICLE
Journal of New Media, Vol.2, No.3, pp. 121-130, 2020, DOI:10.32604/jnm.2020.010674
Abstract With the continuous development of the computer, people's
requirements for computers are also getting more and more, so the brain-computer
interface system (BCI) has become an essential part of computer research. Emotion
recognition is an important task for the computer to understand social status in
BCI. Affective computing (AC) aims to develop the model of emotions and
advance the affective intelligence of computers. There are various emotion
recognition approaches. The method based on electroencephalogram (EEG) is
more reliable because it is higher in accuracy and more objective in evaluation than
other external appearance clues such as emotion expression and gesture.… More >
Open Access
ARTICLE
Journal of New Media, Vol.3, No.1, pp. 19-27, 2021, DOI:10.32604/jnm.2021.018267
Abstract Every day we receive a large amount of information through different
social media and software, and this data and information can be realized with the
advent of data mining methods. In the process of data mining, to solve some
high-dimensional problems, feature selection is carried out in limited training
samples, and effective features are selected. This paper focuses on two Relief
feature selection algorithms: Relief and ReliefF algorithm. The differences
between them and their respective applicable scopes are analyzed. Based on
Relief algorithm, the high weight feature subset is obtained, and the correlation
between features is calculated according to the… More >
Open Access
ARTICLE
Journal of New Media, Vol.3, No.4, pp. 137-150, 2021, DOI:10.32604/jnm.2021.024543
Abstract Internet of Everything (IoE) has emerged as a promising paradigm for
the purpose of connecting and exchanging data among physical objects and
humans over the Internet, and it can be widely applied in the fields of industry,
transportation, commerce, and education. Recently, the emergence of 6G-enabled
cybertwin network architecture provides the technical and theoretical foundation
for the realization of IoE paradigm. However, the IoE has three open issues in the
6G-enabled cybertwin architecture, i.e., data authenticity, data storage and node
reliability. To address these issues, we propose a blockchain-based decentralized
reputation management system (BC-DRMS) for IoE in 6G-enabled Cybertwin
architecture.… More >