2018 7th International Conference on Digital Home (ICDH)最新文献_第4页

Polarimetric SAR Image Classification by Multitask Sparse Representation Learning 基于多任务稀疏表示学习的极化SAR图像分类

2018 7th International Conference on Digital Home (ICDH) Pub Date : 2018-11-01 DOI: 10.1109/ICDH.2018.00013

Bo Li, Ying Li, Minxia Chen

引用次数: 1

[Title page iii] [标题页iii]

2018 7th International Conference on Digital Home (ICDH) Pub Date : 2018-11-01 DOI: 10.1109/icdh.2018.00002

引用次数: 0

From Recommendation to Generation: A Novel Fashion Clothing Advising Framework 从推荐到代:一种新颖的时尚服装建议框架

2018 7th International Conference on Digital Home (ICDH) Pub Date : 2018-11-01 DOI: 10.1109/ICDH.2018.00040

Zilin Yang, Zhuo Su, Yang Yang, Ge Lin

{"title":"From Recommendation to Generation: A Novel Fashion Clothing Advising Framework","authors":"Zilin Yang, Zhuo Su, Yang Yang, Ge Lin","doi":"10.1109/ICDH.2018.00040","DOIUrl":"https://doi.org/10.1109/ICDH.2018.00040","url":null,"abstract":"In the field of clothing recommendation, building a successful recommendation system means giving each user an optimal personalized recommending list. The top ranked clothing in the list are expected to meet a series of user's needs such as preference, taste, style, and consumption level. In online shopping, the most common way is to use user's explicit rating of items. However, user's implicit feedback such as browsing log, collection, and reviews may contains extra information to help model user's preference more accurately. In addition, the recommended clothing should also meet user's consumption level, which is an important factor easily overlooked in recommendation system. In this paper, we combine visual features of clothing images, user's implicit feedback and the price factor to construct a recommendation model based on Siamese network and Bayesian personalized ranking to recommend clothing satisfying user's preference and consumption level. Then on the basis of recommending clothing, we use Generative Adversarial Networks to generate new clothing images and use them to form a compatible collocation to provide fashion suggestions out of datasets.","PeriodicalId":117854,"journal":{"name":"2018 7th International Conference on Digital Home (ICDH)","volume":"24 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133745813","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: 11

Encoding the Models with Object-Aware Feature Basis: A New Analytical Tool for Graphic Applications 基于对象感知特征的模型编码:一种新的图形应用分析工具

2018 7th International Conference on Digital Home (ICDH) Pub Date : 2018-11-01 DOI: 10.1109/ICDH.2018.00060

Nannan Li, Haohao Li, Jiangbei Hu, Shengfa Wang, Zhixun Su, Zhongxuan Luo

{"title":"Encoding the Models with Object-Aware Feature Basis: A New Analytical Tool for Graphic Applications","authors":"Nannan Li, Haohao Li, Jiangbei Hu, Shengfa Wang, Zhixun Su, Zhongxuan Luo","doi":"10.1109/ICDH.2018.00060","DOIUrl":"https://doi.org/10.1109/ICDH.2018.00060","url":null,"abstract":"Feature space analysis is always the most central problem in all kinds of graphic applications, and the acquirement of different kinds of basis for feature space has never been stopped. In this paper, we propose a novel way to analyze the feature space by factorizing it into visually reasonable and physically meaningful basis and corresponding encoders (coefficients). Non-negative matrix factorization (NMF) has previously been shown to be powerful in information retrieval, computer vision and pattern recognition for its physically soundable and additive fashion. By transferring the factorization idea onto tasks of graphic applications, in this paper, we propose a framework for generating new feature basis and encoders for further analysis, which helps empower the downstream graphic applications, including analysis on one single model and joint analysis on a couple of models. Instead of factorizing the matrix composed of images or graphic elements/objects, we propose to apply sparseand-constrained NMF (SAC-NMF) to the feature space that is more general and extendable. And by designing various feature descriptors, we get the base functions for the feature space to enable the analysis of one single model and co-analysis of a list of models. Through the extensive experiments, our analytical framework has exhibited many attractive advantages such as being object-aware, robust, discriminative, extendable, etc.","PeriodicalId":117854,"journal":{"name":"2018 7th International Conference on Digital Home (ICDH)","volume":"144 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115177791","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

Image Stitching Algorithm Based on SURF and Wavelet Transform 基于SURF和小波变换的图像拼接算法

2018 7th International Conference on Digital Home (ICDH) Pub Date : 2018-11-01 DOI: 10.1109/ICDH.2018.00009

Jinxin Ruan, Liying Xie, Yuyan Ruan, Lindong Liu, Qiang Chen, Qian Zhang

引用次数: 12

Blind Estimation of PN Sequence Based on FLO Joint M Estimation for Short-Code DSSS Signals 基于FLO联合M估计的短码DSSS信号PN序列盲估计

2018 7th International Conference on Digital Home (ICDH) Pub Date : 2018-11-01 DOI: 10.1109/ICDH.2018.00051

Xiyan Sun, Zhuo Fan, Yuanfa Ji, Suqing Yan, Shouhua Wang, Weimin Zhen

{"title":"Blind Estimation of PN Sequence Based on FLO Joint M Estimation for Short-Code DSSS Signals","authors":"Xiyan Sun, Zhuo Fan, Yuanfa Ji, Suqing Yan, Shouhua Wang, Weimin Zhen","doi":"10.1109/ICDH.2018.00051","DOIUrl":"https://doi.org/10.1109/ICDH.2018.00051","url":null,"abstract":"When using a singular value decomposition (SVD) algorithm to estimate the pseudo code sequence of shortcode direct sequence spread spectrum (DSSS) signals directly under impulse noise, the pseudo code information extracted by the algorithm will be seriously interfered, and the estimation performance will deteriorate obviously. In this paper, proposed is a pseudo code sequence blind estimation algorithm based on fractional low order(FLO) joint M estimation. Under the condition of known pseudo code rate and pseudo code period, the received signal is segmented by the size of double PN period, and the fractional low order matrix of the received signal is constructed by using this algorithm in order to reduce the noise component, and then the matrix is decomposed by the SVD algorithm. By taking the summation and subtraction operation between the absolute value of the principal component and its complement sets to estimate the position of the out-of-step point of the pseudo code. Finally, the blind estimation of a pseudo code sequence is realized. Simulation results show that the proposed algorithm can greatly improve the performance of pseudo code sequence blind estimation in an impulse noise channel. When the signal-to-noise ratio (SNR) is about -5 db, the accuracy of the]pseudo code estimation can be kept above 90%.","PeriodicalId":117854,"journal":{"name":"2018 7th International Conference on Digital Home (ICDH)","volume":"45 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129163817","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

Encrypted Image Feature Extraction by Privacy-Preserving MFS 基于隐私保护的MFS加密图像特征提取

2018 7th International Conference on Digital Home (ICDH) Pub Date : 2018-11-01 DOI: 10.1109/ICDH.2018.00016

Guoming Chen, Qiang Chen, Xiongyong Zhu, Yiqun Chen

引用次数: 3

Herb Community Detection from TCM Prescription Based on Graph Embedding 基于图嵌入的中药方剂草药群落检测

2018 7th International Conference on Digital Home (ICDH) Pub Date : 2018-11-01 DOI: 10.1109/icdh.2018.00062

Gansen Zhao, Zijing Li, Xinming Wang, Weimin Ning, Xutian Zhuang, Jianfei Wang, Qiang Chen, Zefeng Mo, Bingchuan Chen, Huiyan Chen

{"title":"Herb Community Detection from TCM Prescription Based on Graph Embedding","authors":"Gansen Zhao, Zijing Li, Xinming Wang, Weimin Ning, Xutian Zhuang, Jianfei Wang, Qiang Chen, Zefeng Mo, Bingchuan Chen, Huiyan Chen","doi":"10.1109/icdh.2018.00062","DOIUrl":"https://doi.org/10.1109/icdh.2018.00062","url":null,"abstract":"Taking advantage of machine learning models, many researchers are exploring to dig out valuable information in large Traditional TCM(TCM) data. In view of the problems of existing TCM herb community detection methods, including poor flexibility, poor extensibility, poor performance of the herb network map, the difficulty in handling the network with small granularity, and the poor balance of detection results, this paper innovatively proposes a new herb community detection idea based on Graph Embedding. This idea mainly has three steps. The first step is constructing the TCM prescription network. The second step is mapping every herb node in the network to herb vector. The third step is using common vector clustering algorithm to get herb communities by dividing the network. In this paper, the second step is the core step. In order to reflect one-to-one and one-to-many relationship of herb nodes, this paper proposes two herb vector construction methods based on two Graph Embedding methods, respectively are matrix decomposition method and improved random walk method. In order to evaluate the experiment results, this paper proposes a comprehensive evaluation metrics which combining modularity, balance, and manual analysis and conducts experiments on relevant outpatient prescription record data. Experimental results show that the new herb community detection methods proposed in this paper has great performance in evaluation metrics than the traditional community detection algorithm, at the same time, the proposed vector construction method can also find potential new herb communities and help innovation of constructing prescription.","PeriodicalId":117854,"journal":{"name":"2018 7th International Conference on Digital Home (ICDH)","volume":"494 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134018511","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

Algorithm of Ionospheric Scintillation Monitoring 电离层闪烁监测算法

2018 7th International Conference on Digital Home (ICDH) Pub Date : 2018-11-01 DOI: 10.1109/ICDH.2018.00053

Xiyan Sun, Zheyang Zhang, Yuanfa Ji, Suqing Yan, Wentao Fu, Qidong Chen

引用次数: 2

Hierarchical Ensemble Learning for Alzheimer's Disease Classification 分层集成学习在阿尔茨海默病分类中的应用

2018 7th International Conference on Digital Home (ICDH) Pub Date : 2018-11-01 DOI: 10.1109/ICDH.2018.00047

Ruyue Wang, Hanhui Li, Rushi Lan, S. Luo, Xiaonan Luo

{"title":"Hierarchical Ensemble Learning for Alzheimer's Disease Classification","authors":"Ruyue Wang, Hanhui Li, Rushi Lan, S. Luo, Xiaonan Luo","doi":"10.1109/ICDH.2018.00047","DOIUrl":"https://doi.org/10.1109/ICDH.2018.00047","url":null,"abstract":"In this paper, we propose to tackle the problem of Alzheimer's Disease (AD) classification by a novel Hierarchical Ensemble Learning (HEL) framework. Given an MRI image of a subject, our method will divide it into multiple slices, and generate the classification result in a coarse-to-fine way: First, for each slice, multiple pre-trained deep neural networks are adopted to extract features, and classiflers trained with each type of these features are used to generate the coarse predictions; Second, we employ ensemble learning on the coarse results to generate a refined result for each slice; At last, the given subject is classified based on the refined results aggregated from all slices. Using pre-trained networks for feature extraction can reduce the computational costs of training significantly, and the ensemble of multiple features and predicted results from slices can increase the classification accuracy effectively. Hence, our method can achieve the balance between efficiency and effectiveness. Experimental results show that the HEL framework can obtain notable performance gains with respect to various features and classifiers.","PeriodicalId":117854,"journal":{"name":"2018 7th International Conference on Digital Home (ICDH)","volume":"19 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116833193","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: 3