Hypergraph regularized autoencoder for image-based 3D human pose recovery

Hong, Chaoqun; Chen, Xuhui; Wang, Xiaodong; Tang, Chaohui

doi:10.1016/j.sigpro.2015.10.004

Cited by 38 publications

(13 citation statements)

References 34 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Deep learning is now applied widely in industry and research, and with great success in the fields of image processing and computer vision [7,8,44]. Thus, recently developed NR-VQA algorithms have employed deep learning techniques, such as neural networks [47], convolutional neural networks (CNNs) [2], and deep belief networks [5].…”

mentioning

confidence: 99%

No-Reference Video Quality Assessment Based on the Temporal Pooling of Deep Features

Varga

2019

Neural Process Lett

119

View full text Add to dashboard Cite

Video quality assessment (VQA) is an important element of various applications ranging from automatic video streaming to display technology. Furthermore, visual quality measurements require a balanced investigation of visual content and features. Previous studies have shown that the features extracted from a pretrained convolutional neural network are highly effective for a wide range of applications in image processing and computer vision. In this study, we developed a novel architecture for no-reference VQA based on the features obtained from pretrained convolutional neural networks, transfer learning, temporal pooling, and regression. In particular, we obtained solutions by only applying temporally pooled deep features and without using manually derived features. The proposed architecture was trained based on the recently published Konstanz natural video quality database (KoNViD-1k), which contains 1200 video sequences with authentic distortion unlike other publicly available databases. The experimental results obtained based on KoNViD-1k demonstrated that the proposed method performed better than other state-of-the-art algorithms. Furthermore, these results were confirmed by tests using the LIVE VQA database, which contains artificially distorted videos. Keywords No-reference video quality assessment · Convolutional neural network 1 IntroductionMultimedia technology and digital visual signal processing have developed rapidly during recent decades. Digital images and videos are very easy to create, transmit, store, and share. Owing to these developments, the design of reliable video quality assessment (VQA) algorithms has attracted considerable attention. Consequently, VQA has been the focus of many research studies and patents. Furthermore, the vast volume of user-created digital video content has led to the development of numerous VQA applications, which require reliable and effective quality monitoring [39]. B Domonkos Varga

show abstract

mentioning

confidence: 99%

No-Reference Video Quality Assessment Based on the Temporal Pooling of Deep Features

Varga

2019

Neural Process Lett

119

View full text Add to dashboard Cite

show abstract

“…Deep convolutional autoencoder is a powerful learning model for representation learning and has been widely used for different applications [8,20,21,22,23,24,25,9]. Variational Autoencoder (VAE) [16,26] has become a popular generative model, allowing us to formalize image generation task in the framework of probabilistic graphical models with latent variables.…”

Section: Variational Autoencodermentioning

confidence: 99%

“…We also show that our method can learn powerful embeddings of input face images, which can be used to achieve facial attribute manipulation. Moreover we propose a multi-view feature extraction strategy to extract effective image representations, which can be used to achieve state of the art performance in facial attribute prediction.recovery [8,9,10], image privacy protection [11], unsupervised dimension reduction [12] and many other applications [13,14,15]. Deep convolutional generative models, as a branch of unsupervised learning technique in machine learning, have become an area of active research in recent years.…”

mentioning

confidence: 99%

See 1 more Smart Citation

Improving variational autoencoder with deep feature consistent and generative adversarial training

et al. 2019

View full text Add to dashboard Cite

We present a new method for improving the performances of variational autoencoder (VAE). In addition to enforcing the deep feature consistent principle thus ensuring the VAE output and its corresponding input images to have similar deep features, we also implement a generative adversarial training mechanism to force the VAE to output realistic and natural images. We present experimental results to show that the VAE trained with our new method outperforms state of the art in generating face images with much clearer and more natural noses, eyes, teeth, hair textures as well as reasonable backgrounds. We also show that our method can learn powerful embeddings of input face images, which can be used to achieve facial attribute manipulation. Moreover we propose a multi-view feature extraction strategy to extract effective image representations, which can be used to achieve state of the art performance in facial attribute prediction.recovery [8,9,10], image privacy protection [11], unsupervised dimension reduction [12] and many other applications [13,14,15]. Deep convolutional generative models, as a branch of unsupervised learning technique in machine learning, have become an area of active research in recent years. A generative model trained with a given image database can be useful in several ways. One is to learn the essence of a dataset and generate realistic images similar to those in the dataset from random inputs. The whole dataset is "compressed" into the learned parameters of the model, which are significantly smaller than the size of the training dataset. The other is to learn reusable feature representations from unlabeled image datasets for a variety of supervised learning tasks such as image classification.In this paper, we propose a new method to train the variational autoencoder (VAE)[16] to improve its performance. In particular, we seek to improve the quality of the generated images to make them more realistic and less blurry. To achieve this, we employ objective functions based on deep feature consistent principle [17] and generative adversarial network [18,19] instead of the problematic per-pixel loss functions. The deep feature consistent can help capture important perceptual features such as spatial correlation through the learned convolutional operations, while the adversarial training helps to produce images that reside on the manifold of natural images. We also introduce several techniques to improve the convergence of GAN training in this context. In particular, instead of directly using the generated images and the real images in pixel space, the corresponding deep features extracted from pretrained networks are used to train the generator and the discriminator network. We also propose to further relax the constraint on the output of the discriminator network to balance the image reconstruction loss and the adversarial loss. We present experimental results to show that our new method can generate face images with much clearer facial parts such as eyes, nose, mouth, teeth, ears and hair textur...

show abstract

“…Autoencoder is a feed-forward non-recurrent neural network to learn a representation for a set of features [195]. The output layer of autoencoder has the same number of nodes as in the input layer [196].…”

Section: Dimension Reductionmentioning

confidence: 99%

Pathological Brain Detection by Artificial Intelligence in Magnetic Resonance Imaging Scanning (Invited Review)

Wang¹,

Zhang²,

Zhan³

et al. 2016

PIER

View full text Add to dashboard Cite

Abstract-(Aim) Pathological brain detection (PBD) systems aim to assist and even replace neuroradiologists to make decisions for patients. This review offers a comprehensive and quantitative comparison for PBD systems by artificial intelligence in magnetic resonance imaging (MRI) scanning. (Method) We first investigated four categories of brain diseases, including neoplastic disease, neurodegenerative disease, cerebrovascular disease, and inflammation. Next, we introduced important MRI techniques, such as the shimming, water and fat suppression, and three advanced imaging modalities (functional MRI, diffusion tensor imaging, and magnetic resonance spectroscopic imaging). Then, we discussed four image preprocessing techniques (image denoising, slice selection, brain extraction, spatial normalization, and intensity normalization), seven feature representation techniques (shape, moment, wavelet, statistics, entropy, gray level co-occurrence matrix, and Fourier transform), and two dimension reduction techniques (feature selection and feature extraction). Afterwards, we studied classification related methods: six learning models (decision tree, extreme learning machine, k-nearest neighbors, naive Bayes classifier, support vector machine, feed-forward neural network), five kernel functions (linear, homogeneous and inhomogeneous polynomial, radial basis function, and sigmoid), and three types of optimization methods (evolutionary algorithm, stochastic optimization, and swarm intelligence). (Results) We introduced three benchmark datasets and used K-fold stratified cross validation to avoid overfitting. We presented a detailed quantitative comparison among 44 state-of-theart PBD algorithms and discussed their advantages and limitations. (Discussions) Artificial intelligence is now making stride in the PBD field and enjoys a fair amount of success. In the future, semi-supervised learning and transfer learning techniques may be potential breakthroughs to develop PBD systems.

show abstract

Hypergraph regularized autoencoder for image-based 3D human pose recovery

Cited by 38 publications

References 34 publications

No-Reference Video Quality Assessment Based on the Temporal Pooling of Deep Features

No-Reference Video Quality Assessment Based on the Temporal Pooling of Deep Features

Improving variational autoencoder with deep feature consistent and generative adversarial training

Pathological Brain Detection by Artificial Intelligence in Magnetic Resonance Imaging Scanning (Invited Review)

Contact Info

Product

Resources

About