2017 21st International Conference Information Visualisation (IV)最新文献_第4页

Architecture Proposal for Data Extraction of Chart Images Using Convolutional Neural Network 基于卷积神经网络的图表图像数据提取架构方案

2017 21st International Conference Information Visualisation (IV) Pub Date : 2017-07-01 DOI: 10.1109/iV.2017.37

P. R. S. C. Junior, Alexandre Abreu de Freitas, Rafael Daisuke Akiyama, B. Miranda, Tiago Araújo, Carlos G. R. Santos, B. Meiguins, J. Morais

{"title":"Architecture Proposal for Data Extraction of Chart Images Using Convolutional Neural Network","authors":"P. R. S. C. Junior, Alexandre Abreu de Freitas, Rafael Daisuke Akiyama, B. Miranda, Tiago Araújo, Carlos G. R. Santos, B. Meiguins, J. Morais","doi":"10.1109/iV.2017.37","DOIUrl":"https://doi.org/10.1109/iV.2017.37","url":null,"abstract":"Different information visualization techniques can be found in the literature due to the quantity and variety of data stored in computational systems. In this context, the classification of chart images becomes important because it allows various types of graphs to be detected automatically in different contexts, allowing a more specific processing for each type of visualization, for example, data extraction. Several techniques of image classification can be used, where the most common are based on the extraction of features of the images, and a later classification using these features. However, one technique that has been gaining prominence in the context of image classification is the Convolutional Neural Network (CNN). This technique is based on deep learning and, in a way, encapsulates the feature extraction process. In this way, the proposal of this article is to use an architecture of a client-server based model to do the chart image classification and later data extraction from this image. The main advantage is doing the CNN processing on the server side, so the application does not rely on client device limitations. For this, an image dataset was generated from the web, and it has ten classes of graphs. From the experiments done, it was seen that the use of this technique was feasible, and modifications in the architecture can be made as a proposal to improve the accuracy of the model.","PeriodicalId":410876,"journal":{"name":"2017 21st International Conference Information Visualisation (IV)","volume":"239 ","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114098220","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: 12

Visualization of Constrained Data Using Trigonometric Splines 使用三角样条的约束数据可视化

2017 21st International Conference Information Visualisation (IV) Pub Date : 2017-07-01 DOI: 10.1109/iV.2017.32

Farheen Ibraheem, M. Hussain

引用次数: 3

A Support Environment and a Trial Practice of Hacking Contest with Attack and Defense Style on a Game Website 游戏网站攻防式黑客竞赛的支持环境与试验实践

2017 21st International Conference Information Visualisation (IV) Pub Date : 2017-07-01 DOI: 10.1109/iV.2017.78

M. Nakaya, Masahiro Okawa, M. Nakajima, H. Tominaga

引用次数: 2

Visualization Techniques for the Comparative Analysis of Weighted Free Trees 加权自由树对比分析的可视化技术

2017 21st International Conference Information Visualisation (IV) Pub Date : 2017-07-01 DOI: 10.1109/iV.2017.30

Yuichi Naragino, Kazuo Misue

{"title":"Visualization Techniques for the Comparative Analysis of Weighted Free Trees","authors":"Yuichi Naragino, Kazuo Misue","doi":"10.1109/iV.2017.30","DOIUrl":"https://doi.org/10.1109/iV.2017.30","url":null,"abstract":"A weighted free tree is an undirected connected graph with no cycles whose edges and nodes have weights (positive real numbers). The purpose of our research is to support the comparative analysis of weighted free trees. The fundamental categories of visualization techniques that support comparison are juxtaposition, superposition, and explicit encoding. Juxtaposition is often used for comparison. However, the cognitive load required to grasp a difference is large. Superposition is effective for recognizing a difference, but select information may be difficult to observe. Explicit encoding is an effective technique specialized in a specific task, but representation switching may occur frequently during analysis. Hence, there is the risk of destructing a viewer's mental map, which reduces work efficiency. We divide the working process of analysis into 14 tasks and develop four visualization techniques that can continually perform these tasks. Moreover, we conduct an experiment to investigate whether our proposed techniques are able to perform these tasks. We confirm that these tasks can be performed efficiently by applying our techniques to a case study that compares two trees.","PeriodicalId":410876,"journal":{"name":"2017 21st International Conference Information Visualisation (IV)","volume":"32 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126954770","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: 1

Visual-Interactive k-NDN Method (VIK): A Novel Approach to Visualize and Interact with Content-Based Image Retrieval Systems Regarding Similarity and Diversity 视觉交互k-NDN方法(VIK):一种基于相似性和多样性的基于内容的图像检索系统可视化和交互的新方法

2017 21st International Conference Information Visualisation (IV) Pub Date : 2017-07-01 DOI: 10.1109/iV.2017.41

Rafael L. Dias, S. A. T. Mpinda, Renato Bueno, M. X. Ribeiro

{"title":"Visual-Interactive k-NDN Method (VIK): A Novel Approach to Visualize and Interact with Content-Based Image Retrieval Systems Regarding Similarity and Diversity","authors":"Rafael L. Dias, S. A. T. Mpinda, Renato Bueno, M. X. Ribeiro","doi":"10.1109/iV.2017.41","DOIUrl":"https://doi.org/10.1109/iV.2017.41","url":null,"abstract":"Digital imaging plays an important role in many human activities, such as agriculture and forest management, earth sciences, urban planning, weather forecasting, medical imaging and so on. Processing, exploring and visualizing the inconceivable volumes of such images has turned out to be progressively troublesome. The Content-Based Image Retrieval (CBIR) remains an important issue that finds potential applications, given the place that retrieving digital images similar to a user-defined specification or pattern in huge databases now occupies in the day-to-day. CBIR systems use visual information like color, shape and texture to represent images in feature vectors. In general, there is an inconsistency in the evaluation of similarity between images according to human perception and the results computed by CBIR systems, which is called Semantic Gap. One way to improve CBIR systems is by the addition of techniques to visualize and interact with CBIR regarding similarity and diversity criteria, where the user can participate more actively in the process and steer the results according to its needs. In this paper we present the Visual-Interactive k-NDN Method (ViK): a novel approach to visualize and interact with Content-Based Image Retrieval systems. This paper aims at making use of Visual Data Mining techniques applied to queries in CBIR systems, improving the interpretability of the measure of diversity, applied using fractal analysis, as well as the relevance of results according to the prior knowledge of the user. Therefore, the user takes an active role in the content-based image retrieval, guiding its result and, consequently, reducing the Semantic Gap. Additionally, a better understanding of the diversity and similarity factors involved in the query is supported by visualization and interaction techniques.","PeriodicalId":410876,"journal":{"name":"2017 21st International Conference Information Visualisation (IV)","volume":"89 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124606928","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: 2

An Ontology Driven Framework for Personalized Itinerary Visualization 个性化行程可视化的本体驱动框架

2017 21st International Conference Information Visualisation (IV) Pub Date : 2017-07-01 DOI: 10.1109/iV.2017.51

Essayeh Aroua, Hajer Baazaoui Zghal, M. Abed

引用次数: 0

Usage Data Analytics for Human-Machine Interactions with Flexible Manufacturing Systems: Opportunities and Challenges 柔性制造系统人机交互的使用数据分析:机遇与挑战

2017 21st International Conference Information Visualisation (IV) Pub Date : 2017-07-01 DOI: 10.1109/iV.2017.38

Jari Varsaluoma, H. Väätäjä, T. Heimonen, Katariina Tiitinen, Jaakko Hakulinen, M. Turunen, Harri Nieminen

引用次数: 4

HeatPipe: High Throughput, Low Latency Big Data Heatmap with Spark Streaming HeatPipe:高吞吐量，低延迟的大数据热图与Spark流

2017 21st International Conference Information Visualisation (IV) Pub Date : 2017-07-01 DOI: 10.1109/iV.2017.45

Alexandre Perrot, Romain Bourqui, N. Hanusse, D. Auber

引用次数: 8

Enriched Interactive Videos for Teaching and Learning 丰富的互动视频教学和学习

2017 21st International Conference Information Visualisation (IV) Pub Date : 2017-07-01 DOI: 10.1109/iV.2017.74

Clement Benkada, L. Moccozet

引用次数: 17

Sketch-Based Interactions for Untangling of Force-Directed Graphs 力向图解缠的基于草图的交互

2017 21st International Conference Information Visualisation (IV) Pub Date : 2017-07-01 DOI: 10.1109/iV.2017.64

V. Guchev, Cristina Gena

引用次数: 1