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Person Re-identification Algorithm Based on Siamese LSTM 3

Person Re-identification Algorithm Based on Siamese LSTM

引用

3rd International Conference on Natural Language processing, ICNLP 2021

作者： Li, Daxiang Meng, Rui Liu, Ying Xi'An University of Posts and Telecommunications Ministry of Public Security Key Laboratory of Electronic Information Application Technology for Scene Investigation Xi'An University of Posts and Telecommunications School of Telecommunication and Information Engineering Xi'an710121 China Xi'An University of Posts and Telecommunications College Key Laboratory of Electronic Information Application Technology for Scene Investigation Ministry of Public Security Center for Image and Information Processing Xi'an710121 China

ISBN: (纸本)9781665414111

In this paper, we propose a new siamese long short-term memory (SLSTM) network model to solve the problem that the recognition accuracy is affected by the occlusion of pedestrian images. The backbone network is composed of two modules, CNN and LSTM, in order to use the local detail information of pedestrian images, firstly, the image is divided into blocks and the features of each image sub-block are extracted, then use the LSTM module to learn the dependency relationship between the features of the pedestrian image block, obtain the feature representation of pedestrian images through memory coding;at the same time, A new loss function is designed by combining the new triplet verification loss and Softmax recognition loss to reduce the intra-class difference and increase the inter-class difference. Comparative experiments were carried out on three pedestrian benchmark datasets of PRID-2011, Market-1501 and CUHK03. The experimental results show that the model proposed in this paper can reduce the impact of occlusion phenomena on re-identification, and the experimental results are better than other state-of-the-art methods. © 2021 IEEE.

关键词： Long short-term memory

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Long-term tracking algorithm based on kernelized correlation filter 5th

Long-term tracking algorithm based on kernelized correlation...

引用

5th Euro-China Conference on Intelligent Data Analysis and applications, ECC 2018

作者： Li, Na Wu, Lingfeng Li, Daxiang Center for Image and Information Processing Xi’an University of Posts and Telecommunications Xi’an710121 China Key Laboratory of Electronic Information Application Technology for Scene Investigation Ministry of Public Security Xi’an710121 China International Joint Research Center for Wireless Communication and Information Processing Xi’an710121 China

ISBN: (纸本)9783030037659

KCF (Kernelized Correlation Filter) is a classical tracking algorithm based on correlation filter, which has good performance in short-term tracking. But when the object is partially or fully occluded, or disappeared in the view, KCF doesn’t work well. In this paper, a long-term tracking algorithm based on KCF is proposed. HOG (Histogram of Oriented Gradient) and LAB three-channel color information are employed to represent the object, and a re-detection module is added into the KCF tracking procedure. The peak ratio is introduced to control the start of the re-detection module, and a correlation filter model based on SURF feature points is re-learned to continuously track the occluded object. Experimental results on OTB dataset show that our algorithm has higher tracking accuracy than other five trackers, and is suitable for long-term tracking. © Springer Nature Switzerland AG 2019.

关键词： Object detection

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A Survey of Text Detection Algorithms in images Based on Deep Learning 4

A Survey of Text Detection Algorithms in Images Based on Dee...

引用

4th International Conference on Natural Language processing, ICNLP 2022

作者： Li, Linna Hu, Cuicui Liu, Ying Xi'an University of Posts and Telecommunications Network Public Opinion Monitoring and Analysis Center Shaanxi Xi'an710121 China Xi'an University of Posts and Telecommunications College Key Laboratory of Electronic Information Application Technology for Scene Investigation Ministry of Public Security Center for Image and Information Processing Shaanxi Xi'an710121 China

ISBN: (数字)9781665495448

ISBN: (纸本)9781665495448

Nowadays, text detection has infiltrated various industries like banking, education, criminal investigation, network public opinion, and more. However, the traditional way of text detection is largely dependent on the characteristics of manual design, which is time-consuming, laborious and inaccuracy. With the emergence of deep learning technology, text detection has already been transformed from text detection in static documents to more practical text detection in natural scenes. To address such challenges as complex background, improper acquisition, and multilingual text in natural scene images, there have been various improved neural network structures constructed. Especially, the performance of text detection in various scenes has been significantly improved. In learning in different scenes are summarized in detail. According to the different text objects in detection, the existing methods are classified into two categories as top-down and bottom-up, and the logic structure, advantages and disadvantages of various algorithms are summarized. In addition, the commonly used data sets and performance evaluation indexes are analyzed and explained, the practical application of text detection in different fields is introduced, and the future trend of research and development are indicated. © 2022 IEEE.

关键词： Deep learning

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Fusion CNN Based on Feature Selection for Crime Scene Investigation image Classification 15th

Fusion CNN Based on Feature Selection for Crime Scene Invest...

引用

15th International Conference on Natural Computation, Fuzzy Systems and Knowledge Discovery, ICNC-FSKD 2019, co-located with the 5th International Conference on Harmony Search, Soft Computing and applications, ICHSA 2019

作者： Zhang, Qiannan Liu, Ying Wang, Fuping Lu, Jin Li, Daxiang School of Communication and Information Engineering Xi’an University of Posts and Telecommunications Xi’an710121 China Key Laboratory of Electronic Information Applications Technology for Scene Investigation Ministry of Public Security Xi’an710121 China

ISBN: (纸本)9783030325909

Crime Scene Investigation images have many semantic categories and complex image contents. The Convolution Neural Network (CNN) feature cannot express the uniformity of image content and high dimensional features can lead to redundancy of feature vectors in CNN. In the circumstance it is difficult to use CNN to process crime scene investigation images. To solve the above problems, we propose a fusion CNN algorithm based on feature selection for the classification of crime scene investigation images. In this paper, we build the fusion CNN features to enhance the ability of representation by fusing the convolutional layer with the fully connected layer. Then we select the fusion features with Laplacian score and label mutual information. Finally, we use the obtained features to train Support Vector Machine (SVM) classifier on the Crime Scene Investigation images Database (CSID). Experiments show that the average classification accuracy of the proposed method can reach 93.67%. © 2020, Springer Nature Switzerland AG.

关键词： Feature Selection

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Topic Embedded Representation Enhanced Variational Wasserstein Autoencoder for Text Modeling 5

Topic Embedded Representation Enhanced Variational Wasserste...

引用

5th IEEE International Conference on Electronics technology, ICET 2022

作者： xiang, Zheng Liu, xiaoming Yang, Guan Liu, Yang Zhongyuan University of Technology School of Computer Science Zhengzhou China Henan Key Laboratory on Public Opinion Intelligent Analysis Zhengzhou China Key Laboratory of text processing and image understanding Zhengzhou China Xidian University State Key Laboratory of Integrated Services Networks Xi'an China Shandong University Key Lab of Cryptologic Technology and Information Security Ministry of Education Xi'an China

ISBN: (纸本)9781665485081

Variational Autoencoder (VAE) is now popular in modeling and language generation tasks, which need to pay attention to the diversity of generation results. The existing models are insufficient in capturing the built-in relationships between topic representation and sequential words. At the same time, there is a massive contradiction between the commonly used simple Gaussian prior and the actual complex distribution of languages. To address the above problems, we introduce a hybrid Wasserstein Autoencoder (WAE) with Topic Embedded Representation (TER) for modeling. TER is obtained through an embedding-based topic model and can capture the dependencies and semantic similarities between topics and words. In this case, the learned latent variable has rich semantic knowledge with the help of TER and is easier to explain and control. Our experiments show that our method is competitive with other VAEs in modeling. © 2022 IEEE.

关键词： Semantics

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Correlation Filter Tracking Algorithm Based on Spatio-Temporal Context 15th

Correlation Filter Tracking Algorithm Based on Spatio-Tempor...

引用

作者： Die, Jin Li, Na Liu, Ying Wu, Yangyang School of Communication and Information Engineering Xi’an University of Posts and Telecommunications Xi’an710121 China Key Laboratory of Electronic Information Application Technology for Scene Investigation Ministry of Public Security Xi’an710121 China International Joint Research Center for Wireless Communication and Information Processing Technology Xi’an710121 China

ISBN: (纸本)9783030324551

Object tracking has been widely used in artificial intelligence, military reconnaissance, security monitoring and other fields. It has become a research hotspot of computer vision. To handle the drift problem in the presence of occlusions, a tracker combined with spatio-temporal context information and correlation filter is proposed in this paper. HOG (Histogram of Oriented Gradient), CN (Color Name) and gray features are extracted to learn the correlation filter. Meanwhile, the spatio-temporal context model is trained. The response map of correlation filter and spatio-temporal context model are normalized and fused. Experimental results show that the proposed algorithm can accurately track the object, and has better performance in terms of successful rate, center position error and distance precision. © Springer Nature Switzerland AG, 2020.

关键词： Tracking (position)

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Transformer-based Lossless Compression of Aurora Spectral images: A Spatial-Temporal-Spectral Joint Approach

Transformer-based Lossless Compression of Aurora Spectral Im...

引用

International Conference on Signal processing Proceedings (ICSP)

作者： xinlong Cao Jiaojiao Li Yuanqing Li Jiapeng Yan Zhenxing Zheng School of Communications and Information Engineering & School of Artificial Intelligence Xi'an University of Posts & Telecommunications Xi'an China Xi’an Key Laboratory of Image Processing Technology and Applications for Public Security Xi'an China International Joint Research Center for Wireless Communication and Information Processing Technology of Shaanxi Province Xi'an China

ISBN: (数字)9798350387384

ISBN: (纸本)9798350387391

Aurora spectral image lossless compression has seen significant advancements in recent years. However, most compression algorithms are based on traditional image compression techniques, focusing solely on spectral and spatial correlations without considering temporal correlations. To further enhance compression performance, this paper proposes a Transformer-based point-by-point prediction algorithm for auroral spectral images, which simultaneously uses spatial, spectral and temporal contexts for prediction. Our prediction network consists of two parts: an encoder and a decoder. The encoder is composed of the encoding unit of the original Transformer and is used to extract features from spatial, spectral and temporal contexts. The decoder consists of fully connected layers and is used for prediction. Experimental results show that the average bitrate of this method is reduced by 0.179 bpp compared with the JPEG2000 algorithm, and the average bitrate is reduced by 0.054 bpp compared with the online DPCM algorithm.

关键词： Ion radiation effects image coding Correlation Signal processing algorithms Transformers Prediction algorithms Feature extraction Decoding Magnetosphere Compression algorithms

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Adaptive Robust Watermarking Algorithm Based on image Texture

Adaptive Robust Watermarking Algorithm Based on Image Textur...

引用

作者： xing Yang Ying Liu Tingge Zhu Center for image and information processing Xi'an university of posts and telecommunications College key laboratory of electronic information application technology for scene investigation ministry of public security Center for image and information processing Xi'an University of posts and telecommunications

Digital watermarking is a key technology to solve copyright protection and content authentication. Most existing watermarking algorithms are based on global embedding, which cannot well balance the imperceptibility and robustness of watermarking. This paper proposes an adaptive robust watermarking algorithm based on image texture, which mainly includes:(1) color image is converted from RGB space to Lab space and the stable scale invariant feature transform(SIFT) points are extracted based on L component as the embedding position of watermark;(2)the structured forest edge is extracted using machine learning as the watermark image which is decomposed by using lifting wavelet transform(LWT) and then encrypted using logical chaos transform;(3)in consideration of human visual system, the strength factor is adaptively selected by using the brightness information and texture complexity of the L component. Experimental results show that the proposed algorithm in Lab space has the better visual invisibility and robustness to resist various attacks, especially for cropping, noise and JPEG compression attacks in comparison with other related algorithms.

关键词： Copyright protection Content authentication Adaptive robust watermarking Structured forest edge Lifting wavelet transform

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Tyre Pattern Classification Based on Multi-scale GCN Model

Tyre Pattern Classification Based on Multi-scale GCN Model

引用

作者： Fuping Wang xiaoxiao Ding Ying Liu College key laboratory of electronic information application technology for scene investigation ministry of public security Center for image and information processing Xi'an University of posts &telecommunications Center for image and information processing Xi'an University of posts& telecommunications

Tyre pattern image classification plays an important role in traffic accidents and criminal scene investigation, and it contains rich texture structure information. Classic deep learning models, such as VGG, are often not targeted to represent the texture structure of tyre pattern images, and often cause over-fitting training due to large-scale parameters and insufficient training samples. To improve classification performance of tyre pattern image and solve the model overfitting problem, an efficient tyre pattern image classification model based on multi-scale Gabor convolutional neural network(MS-GCN) is proposed. First, a bank of large-scale directional Gabor filters are used to modulate the convolution kernel to extract more accurate texture features for large-size tyre pattern images, which greatly reduces the number of the training parameters and makes the model more streamlined. Secondly, due to the multi-scale texture similarity of the tyre pattern image, the multi-scale features from different convolutional layers are fused to produce the precise feature representation of the image, following by the optimal feature dimension selection. A large number of experiments were carried out on the real tyre pattern image data *** results showed that the classification accuracy of the proposed algorithm is 95.9%, which is greatly improved compared with the handcrafted feature extraction algorithm and increased by 17.3%compared with deep learning-based model VGG16. In addition, the classification accuracy of the proposed algorithm on the GHIM-10 K data set is 92%, which is also significantly improved compared to other methods. Overall, it shows the effectiveness and superiority of the proposed algorithm.

关键词： Convolutional neural network Gabor filter Multi-scale texture features Tyre pattern image classification

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MGTN: Multi-scale Graph Transformer Network for 3D Point Cloud Semantic Segmentation

MGTN: Multi-scale Graph Transformer Network for 3D Point Clo...

引用

IEEE Visual Communications and image processing (VCIP)

作者： Da Ai Siyu Qin Zihe Nie Hui Yuan Ying Liu Xi’an Key Laboratory of Image Processing Technology and Applications for Public Security Xi’an University of Posts and Telecommunications Xi’an China School of Communication and Information Engineering Xi’an University of Posts and Telecommunications Xi’an China School of Control Science and Engineering Shandong University Jinan China

ISBN: (数字)9798331529543

ISBN: (纸本)9798331529550

The structural similarity of point clouds presents challenges in accurately recognizing and segmenting semantic information at the demarcation points of complex scenes or objects. In this study, we propose a multi-scale graph transformer network (MGTN) for 3D point cloud semantic segmentation. First, a multi-scale graph convolution (MSG-Conv) is devised to address the limitations faced by existing methods when extracting local and global features of point cloud data with varying densities simultaneously. Subsequently, we employ a graph-transformer (G-T) module to enhance edge details and spatial position information in the point cloud, thereby improving recognition accuracy for small objects and confusing elements such as columns and beams. Extensive testing on ShapeNet parts and S3DIS datasets was conducted to demonstrate the effectiveness of MGTN. Compared to the baseline network DGCNN, our proposed MGTN achieves substantial performance improvements, as evidenced by notable increases in mIoU of 1.5% and 18.5% on the ShapeNet parts and S3DIS datasets respectively. Additionally, MGTN outperforms the recent CFSA- Net by 2.3% and 3.4% on OA and mIoU respectively.

关键词： Point cloud compression Three-dimensional displays Visual communication Convolution Semantic segmentation Semantics Transformers Feature extraction Data mining Testing

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