2019 Tenth International Conference on Intelligent Control and Information Processing (ICICIP)最新文献_第4页

An Image Compression Framework Based on Multi-scale Convolutional Neural Network for Deformation Images 基于多尺度卷积神经网络的变形图像压缩框架

2019 Tenth International Conference on Intelligent Control and Information Processing (ICICIP) Pub Date : 2019-12-01 DOI: 10.1109/ICICIP47338.2019.9012196

Zhenbing Liu, Xinlong Li, Weiwei Li, Rushi Lan, Xiaonan Luo

{"title":"An Image Compression Framework Based on Multi-scale Convolutional Neural Network for Deformation Images","authors":"Zhenbing Liu, Xinlong Li, Weiwei Li, Rushi Lan, Xiaonan Luo","doi":"10.1109/ICICIP47338.2019.9012196","DOIUrl":"https://doi.org/10.1109/ICICIP47338.2019.9012196","url":null,"abstract":"Along with the development of deep learning, convolutional neural networks (CNN) have become more and more widely used in the field of image compression, and have made image compression technology significantly improved on performance and cost. In order to make the image better preserve the details and texture of the original image while compressing, we propose a novel method to optimize image compression. We first focus on slight deformation image which is changed by original image, which means that we preserve the features of image detail information when the bits are not increased, and then the deformed image is transmitted to our network framework to achieve image compression and the reconstruction process. In our system, first, a multi-scale convolutional neural network is used to learn the best compact representation from the input image to achieve the purpose of extracting multiscale structural information of natural images. The result of compact representation is then encoded and decoded by a conventional image codec. Finally, a reconstructed convolutional neural network is used for reconstructing the decoded image with high quality and accurate quality at the decoding end. Experimental results demonstrate that our network is superior to most existing methods and can improve image quality with more visual detail.","PeriodicalId":431872,"journal":{"name":"2019 Tenth International Conference on Intelligent Control and Information Processing (ICICIP)","volume":"90 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127587320","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Cloudde-based Distributed Differential Evolution for Solving Dynamic Optimization Problems 基于云的分布式差分进化求解动态优化问题

2019 Tenth International Conference on Intelligent Control and Information Processing (ICICIP) Pub Date : 2019-12-01 DOI: 10.1109/ICICIP47338.2019.9012183

Yueqin Li, Zhi-hui Zhan, Hu Jin, Jun Zhang

引用次数: 5

An Improved SLIC Super-pixel Extraction Algorithm Based on MMTD 基于MMTD的改进SLIC超像素提取算法

2019 Tenth International Conference on Intelligent Control and Information Processing (ICICIP) Pub Date : 2019-12-01 DOI: 10.1109/ICICIP47338.2019.9012172

Ningning Zhou, Yang Liu, Long Hong

{"title":"An Improved SLIC Super-pixel Extraction Algorithm Based on MMTD","authors":"Ningning Zhou, Yang Liu, Long Hong","doi":"10.1109/ICICIP47338.2019.9012172","DOIUrl":"https://doi.org/10.1109/ICICIP47338.2019.9012172","url":null,"abstract":"Super-pixel can improve the computational efficiency of image segmentation algorithm, which is a description of image with more visual significance. The research on super-pixel extraction algorithm has always been a hot spot of current research. SLIC is a common super-pixel extraction algorithm. This method can generate compact and nearly uniform super-pixels. It has a high comprehensive evaluation in the aspects of computing speed, object contour preservation and super-pixel shape. As a result it has been widely used. However, when extracting SLIC (Simple Linear Iterative Clustering) super-pixel blocks, the determination of weight m needs to be specified manually, and the same weight value for different images leads to the problem of unsatisfactory segmentation effect. In order to solve this problem, this paper introduces the medium mathematics used to deal with the fuzzy phenomenon into the super-pixel extraction, and proposes a super-pixel extraction algorithm based on MMTD (Measuring of Medium Truth Scale) and applies it to image segmentation. Firstly, the similarity between Lab distance and coordinate distance is obtained based on MMTD. Then, the weight m of SLIC super-pixel extraction algorithm is adaptively determined by iteration method. Finally, the isolated points are corrected by connected components. The experimental results show that this algorithm has better super-pixel extraction effect than the SLIC algorithm, which can effectively improve the quality of image segmentation in the later stage.","PeriodicalId":431872,"journal":{"name":"2019 Tenth International Conference on Intelligent Control and Information Processing (ICICIP)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131269738","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

A Method for Content-Based Image Retrieval with a Two-Stage Feature Matching 基于内容的两阶段特征匹配图像检索方法

2019 Tenth International Conference on Intelligent Control and Information Processing (ICICIP) Pub Date : 2019-12-01 DOI: 10.1109/ICICIP47338.2019.9012213

Jing Huang, Shuguo Yang, Wenwu Wang

{"title":"A Method for Content-Based Image Retrieval with a Two-Stage Feature Matching","authors":"Jing Huang, Shuguo Yang, Wenwu Wang","doi":"10.1109/ICICIP47338.2019.9012213","DOIUrl":"https://doi.org/10.1109/ICICIP47338.2019.9012213","url":null,"abstract":"Content-based image retrieval is an active area of research where image content is used to guide the search of relevant images from a dataset. Given a query image, the images in the dataset are ranked in terms of their scores of similarity to this image based on their visual appearance. Many existing algorithms are based on either single feature or the fusion of multi-features with a one-step search method, which may lead to undesirable results due to the mismatch between low-level features and high-level semantics. To address this issue, we propose a two-stage sequential search algorithm where the color feature, represented by a color histogram in the HSV space, is used to form an image set containing images of similar color distributions to that of the query image, then a second stage of search is performed via the matching of feature points, in terms of discrete wavelet transform (DWT), and the scale invariant feature transform (SIFT) feature, extracted from a low-frequency subgraph. Experiments are performed on the ZuBuD dataset and UKBench dataset. Compared to some state-of-the-art algorithms, the proposed algorithm gives higher precision score.","PeriodicalId":431872,"journal":{"name":"2019 Tenth International Conference on Intelligent Control and Information Processing (ICICIP)","volume":"202 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132748878","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Design and Analysis of Neural Networks Based on Linearly Translated Features 基于线性翻译特征的神经网络设计与分析

2019 Tenth International Conference on Intelligent Control and Information Processing (ICICIP) Pub Date : 2019-12-01 DOI: 10.1109/ICICIP47338.2019.9012159

Jiasen Wang, Jun Wang, Wei Zhang

引用次数: 0

A Gaussian Data Augmentation Technique on Highly Dimensional, Limited Labeled Data for Multiclass Classification Using Deep Learning 基于深度学习的高维有限标签数据高斯数据增强技术

2019 Tenth International Conference on Intelligent Control and Information Processing (ICICIP) Pub Date : 2019-12-01 DOI: 10.1109/ICICIP47338.2019.9012197

J. Rochac, L. Liang, N. Zhang, T. Oladunni

{"title":"A Gaussian Data Augmentation Technique on Highly Dimensional, Limited Labeled Data for Multiclass Classification Using Deep Learning","authors":"J. Rochac, L. Liang, N. Zhang, T. Oladunni","doi":"10.1109/ICICIP47338.2019.9012197","DOIUrl":"https://doi.org/10.1109/ICICIP47338.2019.9012197","url":null,"abstract":"In recent years, using oceans of data and virtually infinite cloud-based computation power, deep learning models leverage the current state-of-the-art classification to reach expert level performance. Researchers continue to explore applications of deep machine learning models ranging from face-, text- and voice-recognition to signal and information processing. With the continuously increasing data collection capabilities, datasets are becoming larger and more dimensional. However, manually labeled data points cannot keep up. It is this disparity between the high number of features and the low number of labeled samples what motivates a new approach to integrate feature reduction and sample augmentation to deep learning classifiers. This paper explores the performance of such approach on three deep learning classifiers: MLP, CNN, and LSTM. First, we establish a baseline using the original dataset. Second, we preprocess the dataset using principal component analysis (PCA). Third, we preprocess the dataset with PCA followed by our Gaussian data augmentation (GDA) technique. To estimate performance, we add k-fold cross-validation to our experiments and compile our results in a numerical and graphical using the confusion matrix and a classification report that includes accuracy, recall, f-score and support. Our experiments suggest superior classification accuracy of all three classifiers in the presence of our PCA+GDA approach.","PeriodicalId":431872,"journal":{"name":"2019 Tenth International Conference on Intelligent Control and Information Processing (ICICIP)","volume":"157 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115595815","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 4

Significant Wave Height Prediction Based on MSFD Neural Network 基于MSFD神经网络的有效波高预测

2019 Tenth International Conference on Intelligent Control and Information Processing (ICICIP) Pub Date : 2019-12-01 DOI: 10.1109/ICICIP47338.2019.9012194

Huan Wang, Dongyang Fu, Shan Liao, Guancheng Wang, Xiuchun Xiao

引用次数: 2

A Controller of Liquid Material on Fast Saturated Zeroing Dynamics Model in Industrial Agitator Tank 基于工业搅拌槽快速饱和归零动力学模型的液体物料控制器

2019 Tenth International Conference on Intelligent Control and Information Processing (ICICIP) Pub Date : 2019-12-01 DOI: 10.1109/ICICIP47338.2019.9012186

Wenhui Duan, Xiuchun Xiao, Dongyang Fu, Mei Liu, Jiliang Zhang, Shi Yan, Long Jin

引用次数: 0

Adaptive Fuzzy Compensation Control of MIMO Stochastic Nonlinear Systems with Input Hysteresis 具有输入滞后的MIMO随机非线性系统的自适应模糊补偿控制

2019 Tenth International Conference on Intelligent Control and Information Processing (ICICIP) Pub Date : 2019-12-01 DOI: 10.1109/ICICIP47338.2019.9012173

Yuanyuan Xu, Qihe Shan, Tie-shan Li, Min Han

引用次数: 0

The Establishment of Chinese Character Reference Framework for the English Monophthong 英语单音节汉字参考框架的建立

2019 Tenth International Conference on Intelligent Control and Information Processing (ICICIP) Pub Date : 2019-12-01 DOI: 10.1109/ICICIP47338.2019.9012174

Xia He, Long Hong, Guoping Du

引用次数: 0