2016 14th International Workshop on Content-Based Multimedia Indexing (CBMI)最新文献_第2页

A scattering transform combination with local binary pattern for texture classification 结合局部二值模式的散射变换纹理分类

2016 14th International Workshop on Content-Based Multimedia Indexing (CBMI) Pub Date : 2016-06-15 DOI: 10.1109/CBMI.2016.7500238

Vu-Lam Nguyen, Ngoc-Son Vu, P. Gosselin

引用次数: 6

A probabilistic topic approach for context-aware visual attention modeling 上下文感知视觉注意建模的概率主题方法

2016 14th International Workshop on Content-Based Multimedia Indexing (CBMI) Pub Date : 2016-06-15 DOI: 10.1109/CBMI.2016.7500272

M. Fernandez-Torres, I. González-Díaz, F. Díaz-de-María

{"title":"A probabilistic topic approach for context-aware visual attention modeling","authors":"M. Fernandez-Torres, I. González-Díaz, F. Díaz-de-María","doi":"10.1109/CBMI.2016.7500272","DOIUrl":"https://doi.org/10.1109/CBMI.2016.7500272","url":null,"abstract":"The modeling of visual attention has gained much interest during the last few years since it allows to efficiently drive complex visual processes to particular areas of images or video frames. Although the literature concerning bottom-up saliency models is vast, we still lack of generic approaches modeling top-down task and context-driven visual attention. Indeed, many top-down models simply modulate the weights associated to low-level descriptors to learn more accurate representations of visual attention than those ones of the generic fusion schemes in bottom-up techniques. In this paper we propose a hierarchical generic probabilistic framework that decomposes the complex process of context-driven visual attention into a mixture of latent subtasks, each of them being in turn modeled as a combination of specific distributions of low-level descriptors. The inclusion of this intermediate level bridges the gap between low-level features and visual attention and enables more comprehensive representations of the later. Our experiments on a dataset in which videos are organized by genre demonstrate that, by learning specific distributions for each video category, we can notably enhance the system performance.","PeriodicalId":356608,"journal":{"name":"2016 14th International Workshop on Content-Based Multimedia Indexing (CBMI)","volume":"118 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-06-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116357670","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

Explorative hyperbolic-tree-based clustering tool for unsupervised knowledge discovery 无监督知识发现的探索性双曲树聚类工具

2016 14th International Workshop on Content-Based Multimedia Indexing (CBMI) Pub Date : 2016-06-15 DOI: 10.1109/CBMI.2016.7500271

M. Riegler, Konstantin Pogorelov, M. Lux, P. Halvorsen, C. Griwodz, T. Lange, S. Eskeland

引用次数: 11

DeepSketch 2: Deep convolutional neural networks for partial sketch recognition DeepSketch 2:用于部分草图识别的深度卷积神经网络

2016 14th International Workshop on Content-Based Multimedia Indexing (CBMI) Pub Date : 2016-06-15 DOI: 10.1109/CBMI.2016.7500261

S. Dupont, Omar Seddati, S. Mahmoudi

引用次数: 22

Spatial pyramids for boosting global features in content based image retrieval 在基于内容的图像检索中增强全局特征的空间金字塔

2016 14th International Workshop on Content-Based Multimedia Indexing (CBMI) Pub Date : 2016-06-15 DOI: 10.1109/CBMI.2016.7500248

M. Lux, N. Anagnostopoulos, C. Iakovidou

引用次数: 4

EIR — Efficient computer aided diagnosis framework for gastrointestinal endoscopies 用于胃肠道内窥镜检查的高效计算机辅助诊断框架

2016 14th International Workshop on Content-Based Multimedia Indexing (CBMI) Pub Date : 2016-06-15 DOI: 10.1109/CBMI.2016.7500257

M. Riegler, Konstantin Pogorelov, P. Halvorsen, T. Lange, C. Griwodz, P. Schmidt, S. Eskeland, Dag Johansen

{"title":"EIR — Efficient computer aided diagnosis framework for gastrointestinal endoscopies","authors":"M. Riegler, Konstantin Pogorelov, P. Halvorsen, T. Lange, C. Griwodz, P. Schmidt, S. Eskeland, Dag Johansen","doi":"10.1109/CBMI.2016.7500257","DOIUrl":"https://doi.org/10.1109/CBMI.2016.7500257","url":null,"abstract":"Analysis of medical videos for detection of abnormalities like lesions and diseases requires both high precision and recall but also real-time processing for live feedback during standard colonoscopies and scalability for massive population based screening, which can be done using a capsular video endoscope. Existing related work in this field does not provide the necessary combination of detection accuracy and performance. In this paper, a multimedia system is presented where the aim is to tackle automatic analysis of videos from the human gastrointestinal (GI) tract. The system includes the whole pipeline from data collection, processing and analysis, to visualization. The system combines filters using machine learning, image recognition and extraction of global and local image features, and it is built in a modular way, so that it can easily be extended. At the same time, it is developed for efficient processing in order to provide real-time feedback to the doctor. Initial experiments show that our system has detection and localisation accuracy at least as good as existing systems, but it stands out in terms of real-time performance and low resource consumption for scalability.","PeriodicalId":356608,"journal":{"name":"2016 14th International Workshop on Content-Based Multimedia Indexing (CBMI)","volume":"362 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-06-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115953093","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: 35

A user-study examining visualization of lifelogs 一个用户研究检查可视化的生活日志

2016 14th International Workshop on Content-Based Multimedia Indexing (CBMI) Pub Date : 2016-06-15 DOI: 10.1109/CBMI.2016.7500236

Soumyadeb Chowdhury, M. Ferdous, J. Jose

{"title":"A user-study examining visualization of lifelogs","authors":"Soumyadeb Chowdhury, M. Ferdous, J. Jose","doi":"10.1109/CBMI.2016.7500236","DOIUrl":"https://doi.org/10.1109/CBMI.2016.7500236","url":null,"abstract":"With continuous advances in the pervasive sensing and lifelogging technologies for the quantified self, users now can record their daily life activities automatically and seamlessly. In the existing lifelogging research, visualization techniques for presenting the lifelogs and evaluating the effectiveness of such techniques from a lifelogger's perspective has not been adequately studied. In this paper, we investigate the effectiveness of four distinct visualization techniques for exploring the lifelogs, which were collected by 22 lifeloggers who volunteered to use a wearable camera and a GPS device simultaneously, for a period of 3 days. Based on a user study with these 22 lifeloggers, which required them to browse through their personal lifelogs, we seek to identify the most effective visualization technique. Our results suggest various ways to augment and improve the visualization of personal lifelogs to enrich the quality of user experience and making lifelogging tools more engaging. We also propose a new visualization feature-drill-down approach with details-on-demand, to make the lifelogging visualization process more meaningful and informative to the lifeloggers.","PeriodicalId":356608,"journal":{"name":"2016 14th International Workshop on Content-Based Multimedia Indexing (CBMI)","volume":"39 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-06-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116838707","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

Particle physics model for content-based 3D exploration 基于内容的3D探索粒子物理模型

2016 14th International Workshop on Content-Based Multimedia Indexing (CBMI) Pub Date : 2016-06-15 DOI: 10.1109/CBMI.2016.7500259

Miroslav Macík, Jakub Lokoč, Premysl Cech, T. Skopal

引用次数: 1

UCS: Ultimate course search UCS:终极课程搜索

2016 14th International Workshop on Content-Based Multimedia Indexing (CBMI) Pub Date : 2016-06-15 DOI: 10.1109/CBMI.2016.7500242

Sheetal Rajgure, Vincent Oria, Krithika Raghavan, Hardik Dasadia, Sai Shashank Devannagari, Reza Curtmola, J. Geller, P. Gouton, Edina Renfro-Michel, Soon Ae Chun

引用次数: 1

From textual queries to visual queries 从文本查询到视觉查询

2016 14th International Workshop on Content-Based Multimedia Indexing (CBMI) Pub Date : 2016-06-15 DOI: 10.1109/CBMI.2016.7500270

N. Zikos, A. Delopoulos, Dafni Maria Vasilikari

{"title":"From textual queries to visual queries","authors":"N. Zikos, A. Delopoulos, Dafni Maria Vasilikari","doi":"10.1109/CBMI.2016.7500270","DOIUrl":"https://doi.org/10.1109/CBMI.2016.7500270","url":null,"abstract":"In this paper we present a framework to transform textual queries into visual ones. The proposed method uses standard image retrieval techniques with textual queries and the Fast Geometric Consistency Test (FGCT) method. For every textual query a set of images is retrieved and for every image a set of descriptors is extracted. Extracted features are combined with respect to their similarity in their descriptors' space and afterwards with respect to their geometric consistency on the image plane. All pairs of images are tested for consistent geometric structures using the FGCT method. This procedure extracts the subset of images that have a persistent geometric formation in the descriptors' space. Descriptors that compose the persistent formation are extracted and used as the input in a visual query; those features constitute the visual context of the visual query. Afterwards we perform again the FGCT method, but this time using the set of extracted features of the persistent formation into the cloud of images that consists of images with out a priori textual knowledge. It is noteworthy that the proposed method is scale, rotation and translation invariant. Experimental results on the Microsoft's Clickture dataset which consist of 1 million images are presented to support these statements.","PeriodicalId":356608,"journal":{"name":"2016 14th International Workshop on Content-Based Multimedia Indexing (CBMI)","volume":"11 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-06-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125537744","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