A Histopathological Image Feature Representation Method Based on Deep Learning

2015 ◽  
Author(s):  
Gang Zhang ◽  
Ling Zhong ◽  
Yonghui Huang ◽  
Yi Zhang
Keyword(s):  
Deep Learning ◽  
Feature Representation ◽  
Image Feature ◽  
Histopathological Image ◽  
Representation Method

Fast Image Search with Pixel-Based Deep Learning Framework via Efficient Compact Binary Code and Addictive Latent Layer

2017 ◽  
Vol 32 (03) ◽  
pp. 1859004 ◽  
Author(s):  
Jun Yi Li ◽  
Jian Hua Li
Keyword(s):  
Deep Learning ◽  
Nearest Neighbor ◽  
Feature Learning ◽  
Nearest Neighbor Search ◽  
Feature Representation ◽  
Image Feature ◽  
Retrieval Algorithm ◽  
Image Search ◽  
Learning Framework ◽  
Compact Binary

As we know, the nearest neighbor search is a good and effective method for good-sized image search. This paper mainly introduced how to learn an outstanding image feature representation form and a series of compact binary Hash coding functions under deep learning framework. Our concept is that binary codes can be obtained using a hidden layer to present some latent concepts dominating the class labels with usable data labels. Our method is effective in obtaining hash codes and image representations, so it is suitable for good-sized dataset. It is demonstrated in our experiment that the performances of the proposed algorithms were then verified on three different databases, MNIST, CIFAR-10 and Caltech-101. The experimental results reveal that two-proposed image Hash retrieval algorithm based on pixel-level automatic feature learning show higher search accuracy than the other algorithms; moreover, these two algorithms were proved to be more favorable in scalability and generality.

scholarly journals Hyperspectral Remote Sensing Image Feature Representation Method Based on CAE-H with Nuclear Norm Constraint

Electronics ◽  
2021 ◽  
Vol 10 (21) ◽  
pp. 2667
Author(s):  
Xiaodong Yu ◽  
Rui Ding ◽  
Jingbo Shao ◽  
Xiaohui Li
Keyword(s):  
Remote Sensing ◽  
Remote Sensing Image ◽  
Hyperspectral Remote Sensing ◽  
Feature Representation ◽  
Nuclear Norm ◽  
Image Feature ◽  
Frobenius Norm ◽  
Representation Method ◽  
Norm Constraint ◽  
Hyperspectral Remote Sensing Image

Due to the high dimensionality and high data redundancy of hyperspectral remote sensing images, it is difficult to maintain the nonlinear structural relationship in the dimensionality reduction representation of hyperspectral data. In this paper, a feature representation method based on high order contractive auto-encoder with nuclear norm constraint (CAE-HNC) is proposed. By introducing Jacobian matrix in the CAE of the nuclear norm constraint, the nuclear norm has better sparsity than the Frobenius norm and can better describe the local low dimension of the data manifold. At the same time, a second-order penalty term is added, which is the Frobenius norm of the Hessian matrix expressed in the hidden layer of the input, encouraging a smoother low-dimensional manifold geometry of the data. The experiment of hyperspectral remote sensing image shows that CAE-HNC proposed in this paper is a compact and robust feature representation method, which provides effective help for the ground object classification and target recognition of hyperspectral remote sensing image.

Deep learning based feature representation for automated skin histopathological image annotation

2017 ◽  
Vol 77 (8) ◽  
pp. 9849-9869 ◽  
Author(s):  
Gang Zhang ◽  
Ching-Hsien Robert Hsu ◽  
Huadong Lai ◽  
Xianghan Zheng
Keyword(s):  
Deep Learning ◽  
Image Annotation ◽  
Feature Representation ◽  
Histopathological Image

A Image Decomposition Method Based on Euler Mapping for Face Recognition

2014 ◽  
Vol 989-994 ◽  
pp. 4119-4122
Author(s):  
Zhao Kui Li ◽  
Yan Wang
Keyword(s):  
Feature Vector ◽  
State Of The Art ◽  
Image Decomposition ◽  
Feature Representation ◽  
Image Feature ◽  
Z Score ◽  
Linear Discriminant ◽  
Score Method ◽  
Representation Method ◽  
Low Dimensional

This paper presents a robust but simple image feature representation method, called image decomposition based on Euler mapping (IDEM). IDEM firstly captures the orientation information by implementing arctangent operator for each pixel. Then, the orientation image is decomposed into two mapping images by executing Euler mapping. Each mapping image is normalized using the “z-score” method, and all normalized vectors are concatenated into an augmented feature vector. The dimensionality of the augmented feature vector is reduced by linear discriminant analysis to yield a low-dimensional feature vector. Experimental results show that IDEM achieves better results in comparison with state-of-the-art methods.

scholarly journals Text Sentiment Analysis of German Multilevel Features Based on Self-Attention Mechanism

2021 ◽  
Vol 2021 ◽  
pp. 1-12
Author(s):  
Xiang Li
Keyword(s):  
Feature Extraction ◽  
Deep Learning ◽  
Feature Representation ◽  
The Self ◽  
Word Level ◽  
Sentence Level ◽  
Improvement Effect ◽  
Representation Method ◽  
Text Sentiment Analysis ◽  
Deep Learning Model

In this paper, we propose a multilevel feature representation method that combines word-level features, such as German morphology and slang, and sentence-level features, such as special symbols and English-translated sentiment information, and build a deep learning model for German sentiment classification based on the self-attentive mechanism, in order to address the characteristics of German social media texts that are colloquial, irregular, and diverse. Compared with the existing studies, this model not only has the most obvious improvement effect but also has better feature extraction and classification ability for German emotion.

scholarly journals Image Representation Method Based on Relative Layer Entropy for Insulator Recognition

Entropy ◽  
2020 ◽  
Vol 22 (4) ◽  
pp. 419
Author(s):  
Zhenbing Zhao ◽  
Hongyu Qi ◽  
Xiaoqing Fan ◽  
Guozhi Xu ◽  
Yincheng Qi ◽  
...  
Keyword(s):  
Image Representation ◽  
Recognition Task ◽  
Training Sample ◽  
Feature Representation ◽  
Image Feature ◽  
Feature Maps ◽  
Deep Convolutional Neural Networks ◽  
Object Appearance ◽  
Indoor Scenes ◽  
Representation Method

Deep convolutional neural networks (DCNNs) with alternating convolutional, pooling and decimation layers are widely used in computer vision, yet current works tend to focus on deeper networks with many layers and neurons, resulting in a high computational complexity. However, the recognition task is still challenging for insufficient and uncomprehensive object appearance and training sample types such as infrared insulators. In view of this, more attention is focused on the application of a pretrained network for image feature representation, but the rules on how to select the feature representation layer are scarce. In this paper, we proposed a new concept, the layer entropy and relative layer entropy, which can be referred to as an image representation method based on relative layer entropy (IRM_RLE). It was designed to excavate the most suitable convolution layer for image recognition. First, the image was fed into an ImageNet pretrained DCNN model, and deep convolutional activations were extracted. Then, the appropriate feature layer was selected by calculating the layer entropy and relative layer entropy of each convolution layer. Finally, the number of the feature map was selected according to the importance degree and the feature maps of the convolution layer, which were vectorized and pooled by VLAD (vector of locally aggregated descriptors) coding and quantifying for final image representation. The experimental results show that the proposed approach performs competitively against previous methods across all datasets. Furthermore, for the indoor scenes and actions datasets, the proposed approach outperforms the state-of-the-art methods.

Hierarchical Quality-Relevant Feature Representation for Soft Sensor Modeling: A Novel Deep Learning Strategy

2020 ◽  
Vol 16 (6) ◽  
pp. 3721-3730 ◽  
Author(s):  
Xiaofeng Yuan ◽  
Jiao Zhou ◽  
Biao Huang ◽  
Yalin Wang ◽  
Chunhua Yang ◽  
...  
Keyword(s):  
Deep Learning ◽  
Learning Strategy ◽  
Soft Sensor ◽  
Feature Representation ◽  
Relevant Feature ◽  
Sensor Modeling

scholarly journals A Lightweight Fusion Distillation Network for Image Deblurring and Deraining

Sensors ◽  
2021 ◽  
Vol 21 (16) ◽  
pp. 5312
Author(s):  
Yanni Zhang ◽  
Yiming Liu ◽  
Qiang Li ◽  
Jianzhong Wang ◽  
Miao Qi ◽  
...  
Keyword(s):  
Deep Learning ◽  
Image Deblurring ◽  
Image Features ◽  
Image Feature ◽  
Model Complexity ◽  
Small Scale ◽  
Feature Maps ◽  
Learning Framework ◽  
Channel Information ◽  
Scale Spaces

Recently, deep learning-based image deblurring and deraining have been well developed. However, most of these methods fail to distill the useful features. What is more, exploiting the detailed image features in a deep learning framework always requires a mass of parameters, which inevitably makes the network suffer from a high computational burden. We propose a lightweight fusion distillation network (LFDN) for image deblurring and deraining to solve the above problems. The proposed LFDN is designed as an encoder–decoder architecture. In the encoding stage, the image feature is reduced to various small-scale spaces for multi-scale information extraction and fusion without much information loss. Then, a feature distillation normalization block is designed at the beginning of the decoding stage, which enables the network to distill and screen valuable channel information of feature maps continuously. Besides, an information fusion strategy between distillation modules and feature channels is also carried out by the attention mechanism. By fusing different information in the proposed approach, our network can achieve state-of-the-art image deblurring and deraining results with a smaller number of parameters and outperform the existing methods in model complexity.

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