Answer Selection Based on Aligned Local Composite Features and Global Features

Enhancement of Cultural Heritage such as historical images is very crucial to safeguard the diversity of cultures. Automated colorization of black and white images has been subject to extensive research through computer vision and machine learning techniques. Our research addresses the problem of generating a plausible colored photograph of ancient, historically black, and white images of Nepal using deep learning techniques without direct human intervention. Motivated by the recent success of deep learning techniques in image processing, a feed-forward, deep Convolutional Neural Network (CNN) in combination with Inception- ResnetV2 is being trained by sets of sample images using back-propagation to recognize the pattern in RGB and grayscale values. The trained neural network is then used to predict two a* and b* chroma channels given grayscale, L channel of test images. CNN vividly colorizes images with the help of the fusion layer accounting for local features as well as global features. Two objective functions, namely, Mean Squared Error (MSE) and Peak Signal-to-Noise Ratio (PSNR), are employed for objective quality assessment between the estimated color image and its ground truth. The model is trained on the dataset created by ourselves with 1.2 K historical images comprised of old and ancient photographs of Nepal, each having 256 × 256 resolution. The loss i.e., MSE, PSNR, and accuracy of the model are found to be 6.08%, 34.65 dB, and 75.23%, respectively. Other than presenting the training results, the public acceptance or subjective validation of the generated images is assessed by means of a user study where the model shows 41.71% of naturalness while evaluating colorization results.

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Perceptual image hashing based on three-dimensional global features and image energy

IEEE Access ◽

10.1109/access.2021.3069045 ◽

2021 ◽

pp. 1-1

Author(s):

Xiaoran Yuan ◽

Yan Zhao

Keyword(s):

Three Dimensional ◽

Image Hashing ◽

Global Features

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Intelligent Recognition Method of Human Motion Global Features Based on Kinect Skeleton Information

2020 IEEE International Conference on Industrial Application of Artificial Intelligence (IAAI) ◽

10.1109/iaai51705.2020.9332893 ◽

2020 ◽

Author(s):

Lei Tao

Keyword(s):

Human Motion ◽

Recognition Method ◽

Global Features ◽

Intelligent Recognition

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Extraction of local and global features by a convolutional neural network–long short-term memory network for diagnosing bearing faults

Proceedings of the Institution of Mechanical Engineers Part C Journal of Mechanical Engineering Science ◽

10.1177/09544062211016505 ◽

2021 ◽

pp. 095440622110165

Author(s):

Zhang Chao ◽

Wang Wei-zhi ◽

Zhang Chen ◽

Fan Bin ◽

Wang Jian-guo ◽

...

Keyword(s):

Neural Network ◽

Fault Diagnosis ◽

Condition Monitoring ◽

Short Term Memory ◽

Vibration Signal ◽

Short Term ◽

Global Features ◽

Term Memory ◽

Long Short Term Memory ◽

Lstm Network

Accurate and reliable fault diagnosis is one of the key and difficult issues in mechanical condition monitoring. In recent years, Convolutional Neural Network (CNN) has been widely used in mechanical condition monitoring, which is also a great breakthrough in the field of bearing fault diagnosis. However, CNN can only extract local features of signals. The model accuracy and generalization of the original vibration signals are very low in the process of vibration signal processing only by CNN. Based on the above problems, this paper improves the traditional convolution layer of CNN, and builds the learning module (local feature learning block, LFLB) of the local characteristics. At the same time, the Long Short-Term Memory (LSTM) is introduced into the network, which is used to extract the global features. This paper proposes the new neural network—improved CNN-LSTM network. The extracted deep feature is used for fault classification. The improved CNN-LSTM network is applied to the processing of the vibration signal of the faulty bearing collected by the bearing failure laboratory of Inner Mongolia University of science and technology. The results show that the accuracy of the improved CNN-LSTM network on the same batch test set is 98.75%, which is about 24% higher than that of the traditional CNN. The proposed network is applied to the bearing data collection of Western Reserve University under the condition that the network parameters remain unchanged. The experiment shows that the improved CNN-LSTM network has better generalization than the traditional CNN.

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Robust visual servoing using global features based on random process

International Journal of Computational Vision and Robotics ◽

10.1504/ijcvr.2015.068803 ◽

2015 ◽

Vol 5 (2) ◽

pp. 138 ◽

Cited By ~ 1

Author(s):

Laroussi Hammouda ◽

Khaled Kaaniche ◽

Hassen Mekki ◽

Mohamed Chtourou

Keyword(s):

Random Process ◽

Visual Servoing ◽

Global Features

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Efficient disease detection in gastrointestinal videos – global features versus neural networks

Multimedia Tools and Applications ◽

10.1007/s11042-017-4989-y ◽

2017 ◽

Vol 76 (21) ◽

pp. 22493-22525 ◽

Cited By ~ 22

Author(s):

Konstantin Pogorelov ◽

Michael Riegler ◽

Sigrun Losada Eskeland ◽

Thomas de Lange ◽

Dag Johansen ◽

...

Keyword(s):

Neural Networks ◽

Disease Detection ◽

Global Features

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On the analysis of local and global features for hyperemia grading

10.1117/12.2268804 ◽

2017 ◽

Cited By ~ 1

Author(s):

L. Sánchez ◽

N. Barreira ◽

N. Sánchez ◽

A. Mosquera ◽

H. Pena-Verdeal ◽

...

Keyword(s):

Global Features ◽

Local And Global Features

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A Visual and VAE Based Hierarchical Indoor Localization Method

Sensors ◽

10.3390/s21103406 ◽

2021 ◽

Vol 21 (10) ◽

pp. 3406

Author(s):

Jie Jiang ◽

Yin Zou ◽

Lidong Chen ◽

Yujie Fang

Keyword(s):

Image Retrieval ◽

Indoor Localization ◽

Data Sets ◽

Indoor Environments ◽

Global Features ◽

Data Set ◽

Data Annotation ◽

Wide Range ◽

Annotation Costs ◽

Global And Local

Precise localization and pose estimation in indoor environments are commonly employed in a wide range of applications, including robotics, augmented reality, and navigation and positioning services. Such applications can be solved via visual-based localization using a pre-built 3D model. The increase in searching space associated with large scenes can be overcome by retrieving images in advance and subsequently estimating the pose. The majority of current deep learning-based image retrieval methods require labeled data, which increase data annotation costs and complicate the acquisition of data. In this paper, we propose an unsupervised hierarchical indoor localization framework that integrates an unsupervised network variational autoencoder (VAE) with a visual-based Structure-from-Motion (SfM) approach in order to extract global and local features. During the localization process, global features are applied for the image retrieval at the level of the scene map in order to obtain candidate images, and are subsequently used to estimate the pose from 2D-3D matches between query and candidate images. RGB images only are used as the input of the proposed localization system, which is both convenient and challenging. Experimental results reveal that the proposed method can localize images within 0.16 m and 4° in the 7-Scenes data sets and 32.8% within 5 m and 20° in the Baidu data set. Furthermore, our proposed method achieves a higher precision compared to advanced methods.

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