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.

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.

scholarly journals Content-Based Image Retrieval and Feature Extraction: A Comprehensive Review

2019 ◽  
Vol 2019 ◽  
pp. 1-21 ◽  
Author(s):  
Afshan Latif ◽  
Aqsa Rasheed ◽  
Umer Sajid ◽  
Jameel Ahmed ◽  
Nouman Ali ◽  
...  
Keyword(s):  
Computer Vision ◽  
Feature Extraction ◽  
Image Retrieval ◽  
Image Classification ◽  
Image Representation ◽  
Feature Representation ◽  
Image Feature ◽  
Content Based Image Retrieval ◽  
Comprehensive Review ◽  
Visual Understanding

Multimedia content analysis is applied in different real-world computer vision applications, and digital images constitute a major part of multimedia data. In last few years, the complexity of multimedia contents, especially the images, has grown exponentially, and on daily basis, more than millions of images are uploaded at different archives such as Twitter, Facebook, and Instagram. To search for a relevant image from an archive is a challenging research problem for computer vision research community. Most of the search engines retrieve images on the basis of traditional text-based approaches that rely on captions and metadata. In the last two decades, extensive research is reported for content-based image retrieval (CBIR), image classification, and analysis. In CBIR and image classification-based models, high-level image visuals are represented in the form of feature vectors that consists of numerical values. The research shows that there is a significant gap between image feature representation and human visual understanding. Due to this reason, the research presented in this area is focused to reduce the semantic gap between the image feature representation and human visual understanding. In this paper, we aim to present a comprehensive review of the recent development in the area of CBIR and image representation. We analyzed the main aspects of various image retrieval and image representation models from low-level feature extraction to recent semantic deep-learning approaches. The important concepts and major research studies based on CBIR and image representation are discussed in detail, and future research directions are concluded to inspire further research in this area.

scholarly journals Research on Vocabulary Sizes and Codebook Universality

2014 ◽  
Vol 2014 ◽  
pp. 1-7
Author(s):  
Wei-Xue Liu ◽  
Jian Hou ◽  
Hamid Reza Karimi
Keyword(s):  
Image Representation ◽  
Classification Performance ◽  
Image Feature ◽  
Vocabulary Size ◽  
Image Descriptors ◽  
Knn Classifier ◽  
Single Dataset ◽  
Representation Method ◽  
Almost All ◽  
Local Image

Codebook is an effective image representation method. By clustering in local image descriptors, a codebook is shown to be a distinctive image feature and widely applied in object classification. In almost all existing works on codebooks, the building of the visual vocabulary follows a basic routine, that is, extracting local image descriptors and clustering with a user-designated number of clusters. The problem with this routine lies in that building a codebook for each single dataset is not efficient. In order to deal with this problem, we investigate the influence of vocabulary sizes on classification performance and vocabulary universality with the kNN classifier. Experimental results indicate that, under the condition that the vocabulary size is large enough, the vocabularies built from different datasets are exchangeable and universal.

scholarly journals Fabric defect recognition using optimized neural networks

2019 ◽  
Vol 14 ◽  
pp. 155892501989739 ◽  
Author(s):  
Zhoufeng Liu ◽  
Chi Zhang ◽  
Chunlei Li ◽  
Shumin Ding ◽  
Yan Dong ◽  
...  
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Convolutional Neural Network ◽  
Recognition Task ◽  
Deep Convolutional Neural Network ◽  
Image Feature ◽  
Feature Maps ◽  
Convolutional Network ◽  
Fabric Defect ◽  
Defect Recognition

Fabric defect recognition is an important measure for quality control in a textile factory. This article utilizes a deep convolutional neural network to recognize defects in fabrics that have complicated textures. Although convolutional neural networks are very powerful, a large number of parameters consume considerable computation time and memory bandwidth. In real-world applications, however, the fabric defect recognition task needs to be carried out in a timely fashion on a computation-limited platform. To optimize a deep convolutional neural network, a novel method is introduced to reveal the input pattern that originally caused a specific activation in the network feature maps. Using this visualization technique, this study visualizes the features in a fully trained convolutional model and attempts to change the architecture of original neural network to reduce computational load. After a series of improvements, a new convolutional network is acquired that is more efficient to the fabric image feature extraction, and the computation load and the total number of parameters in the new network is 23% and 8.9%, respectively, of the original model. The proposed neural network is specifically tailored for fabric defect recognition in resource-constrained environments. All of the source code and pretrained models are available online at https://github.com/ZCmeteor .

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 Toward Improving Image Retrieval via Global Saliency Weighted Feature

2021 ◽  
Vol 10 (4) ◽  
pp. 249
Author(s):  
Hongwei Zhao ◽  
Jiaxin Wu ◽  
Danyang Zhang ◽  
Pingping Liu
Keyword(s):  
Image Retrieval ◽  
Multiple Scales ◽  
Feature Representation ◽  
Image Feature ◽  
Full Description ◽  
Data Sets ◽  
Global Information ◽  
Feature Maps ◽  
Salient Region ◽  
Information Image

For full description of images’ semantic information, image retrieval tasks are increasingly using deep convolution features trained by neural networks. However, to form a compact feature representation, the obtained convolutional features must be further aggregated in image retrieval. The quality of aggregation affects retrieval performance. In order to obtain better image descriptors for image retrieval, we propose two modules in our method. The first module is named generalized regional maximum activation of convolutions (GR-MAC), which pays more attention to global information at multiple scales. The second module is called saliency joint weighting, which uses nonparametric saliency weighting and channel weighting to focus feature maps more on the salient region without discarding overall information. Finally, we fuse the two modules to obtain more representative image feature descriptors that not only consider the global information of the feature map but also highlight the salient region. We conducted experiments on multiple widely used retrieval data sets such as roxford5k to verify the effectiveness of our method. The experimental results prove that our method is more accurate than the state-of-the-art methods.

scholarly journals Knowledge-Embedded Representation Learning for Fine-Grained Image Recognition

2018 ◽  
Author(s):  
Tianshui Chen ◽  
Liang Lin ◽  
Riquan Chen ◽  
Yang Wu ◽  
Xiaonan Luo
Keyword(s):  
Neural Network ◽  
Knowledge Representation ◽  
Image Recognition ◽  
Feature Learning ◽  
Representation Learning ◽  
Feature Representation ◽  
Image Feature ◽  
Knowledge Graph ◽  
Feature Maps ◽  
Fine Grained

Humans can naturally understand an image in depth with the aid of rich knowledge accumulated from daily lives or professions. For example, to achieve fine-grained image recognition (e.g., categorizing hundreds of subordinate categories of birds) usually requires a comprehensive visual concept organization including category labels and part-level attributes. In this work, we investigate how to unify rich professional knowledge with deep neural network architectures and propose a Knowledge-Embedded Representation Learning (KERL) framework for handling the problem of fine-grained image recognition. Specifically, we organize the rich visual concepts in the form of knowledge graph and employ a Gated Graph Neural Network to propagate node message through the graph for generating the knowledge representation. By introducing a novel gated mechanism, our KERL framework incorporates this knowledge representation into the discriminative image feature learning, i.e., implicitly associating the specific attributes with the feature maps. Compared with existing methods of fine-grained image classification, our KERL framework has several appealing properties: i) The embedded high-level knowledge enhances the feature representation, thus facilitating distinguishing the subtle differences among subordinate categories. ii) Our framework can learn feature maps with a meaningful configuration that the highlighted regions finely accord with the nodes (specific attributes) of the knowledge graph. Extensive experiments on the widely used Caltech-UCSD bird dataset demonstrate the superiority of our KERL framework over existing state-of-the-art methods.

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.

scholarly journals A Novel Detector Based on Convolution Neural Networks for Multiscale SAR Ship Detection in Complex Background

Sensors ◽  
2020 ◽  
Vol 20 (9) ◽  
pp. 2547 ◽  
Author(s):  
Wenxin Dai ◽  
Yuqing Mao ◽  
Rongao Yuan ◽  
Yijing Liu ◽  
Xuemei Pu ◽  
...  
Keyword(s):  
Region Of Interest ◽  
Feature Representation ◽  
Feature Maps ◽  
Sar Images ◽  
Feature Map ◽  
The Public ◽  
Single Feature ◽  
Great Performance ◽  
Residual Block ◽  
Fusion Feature

Convolution neural network (CNN)-based detectors have shown great performance on ship detections of synthetic aperture radar (SAR) images. However, the performance of current models has not been satisfactory enough for detecting multiscale ships and small-size ones in front of complex backgrounds. To address the problem, we propose a novel SAR ship detector based on CNN, which consist of three subnetworks: the Fusion Feature Extractor Network (FFEN), Region Proposal Network (RPN), and Refine Detection Network (RDN). Instead of using a single feature map, we fuse feature maps in bottom–up and top–down ways and generate proposals from each fused feature map in FFEN. Furthermore, we further merge features generated by the region-of-interest (RoI) pooling layer in RDN. Based on the feature representation strategy, the CNN framework constructed can significantly enhance the location and semantics information for the multiscale ships, in particular for the small ships. On the other hand, the residual block is introduced to increase the network depth, through which the detection precision could be further improved. The public SAR ship dataset (SSDD) and China Gaofen-3 satellite SAR image are used to validate the proposed method. Our method shows excellent performance for detecting the multiscale and small-size ships with respect to some competitive models and exhibits high potential in practical application.

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