Konstantin Pogorelov
Michael Riegler
Pål Halvorsen
ABSTRACT
Exploring and annotating collections of images without meta-data is a complex task which requires convenient ways of presenting datasets to a user. Visual analytics and information visualization can help users by providing interfaces, and in this paper, we present an open source application that allows users from any domain to use feature-based clustering of large image collections to perform explorative browsing and annotation. For this, we use various image feature extraction mechanisms, different unsupervised clustering algorithms and hierarchical image collection visualization. The performance of the presented open source software allows users to process and display thousands of images at the same time by utilizing heterogeneous resources such as GPUs and different optimization techniques.
- Yi Gu, Chaoli Wang, Jun Ma, Robert J Nemiroff, and David L Kao. 2015. iGraph: a graph-based technique for visual analytics of image and text collections. In Proc. of IS&T/SPIE Electronic Imaging. 939708--939708.Google Scholar
- Mark Hall, Eibe Frank, Geoffrey Holmes, Bernhard Pfahringer, Peter Reutemann, and Ian H Witten. 2009. The WEKA data mining software: an update. ACM SIGKDD explorations newsletter 11, 1 (2009), 10--18. Google ScholarDigital Library
- John Lamping, Ramana Rao, and Peter Pirolli. 1995. A focus+ context technique based on hyperbolic geometry for visualizing large hierarchies. In Proc. of ACM CHI. 401--408. Google ScholarDigital Library
- Mathias Lux, Michael Riegler, Pål Halvorsen, Konstantin Pogorelov, and Nektar- ios Anagnostopoulos. 2016. LIRE: open source visual information retrieval. In Proc. of MMSys. Article no. 30. Google ScholarDigital Library
- John C Platt, Michal Czerwinski, and Brent A Field. 2003. PhotoTOC: Automatic clustering for browsing personal photographs. In Proc. of ICICS-PAM. 6--10.Google ScholarCross Ref
- Konstantin Pogorelov, Michael Riegler, Pål Halvorsen, Peter Thelin Schmidt, Carsten Griwodz, Dag Johansen, Sigrun L. Eskeland, and Thomas de Lange. 2016. GPU-accelerated Real-time Gastrointestinal Diseases Detection. In Proc. of CBMS. 185--190.Google ScholarCross Ref
- Marco Porta. 2006. Browsing large collections of images through unconventional visualization techniques. In Proc. of AVI. 440--444. Google ScholarDigital Library
- Marco Porta. 2009. New visualization modes for effective image presentation. International Journal of Image and Graphics 9, 01 (2009), 27--49.Google ScholarCross Ref
- Michael Riegler, Carsten Griwodz, Concetto Spampinato, Thomas de Lange, Sigrun L. Eskeland, Konstantin Pogorelov, Wallapak Tavanapong, Peter Thelin Schmidt, Cathal Gurrin, Dag Johansen, Håvard Johansen, and Pål Halvorsen. 2016. Multimedia and Medicine: Teammates for Better Disease Detection and Survival. In Proc. of ACM MM. 968--977. Google ScholarDigital Library
- Michael Riegler, Konstantin Pogorelov, Sigrun Losada Eskeland, Peter The- lin Schmidt, Zeno Albisser, Dag Johansen, Carsten Griwodz, Pål Halvorsen, and Thomas de Lange. 2017. From Annotation to Computer Aided Diagnosis: Detailed Evaluation of a Medical Multimedia System. Transactions on Multimedia Computing, Communications and Applications 9, 4 (2017). Google ScholarDigital Library
- Michael Riegler, Konstantin Pogorelov, Pål Halvorsen, Thomas de Lange, Carsten Griwodz, Peter Thelin Schmidt, Sigrun Losada Eskeland, and Dag Johansen. 2016. EIR - Efficient Computer Aided Diagnosis Framework for Gastrointestinal endoscopies. In Proc. of CBMI. 1--6.Google ScholarCross Ref
- Michael Riegler, Konstantin Pogorelov, Mathias Lux, Pål Halvorsen, Carsten Griwodz, Thomas de Lange, and Sigrun Losada Eskeland. 2016. Explorative Hyperbolic-Tree-Based Clustering Tool for Unsupervised Knowledge Discovery. In Proc. of CBMI. 1--4.Google ScholarCross Ref
- Ricardo S Torres, Celmar G Silva, Claudia B Medeiros, and Heloisa V Rocha. 2003. Visual structures for image browsing. In Proc. of ACM CIKM. 49--55. Google ScholarDigital Library
- Chaoli Wang, John P Reese, Huan Zhang, Jun Tao, and Robert J Nemiroff. 2013. iMap: A stable layout for navigating large image collections with embedded search. In Proc. of IS&T/SPIE Electronic Imaging. 86540K--86540K.Google ScholarCross Ref
- Jing Yang, Jianping Fan, Daniel Hubball, Yuli Gao, Hangzai Luo, William Rib- arsky, and Matthew Ward. 2006. Semantic image browser: Bridging information visualization with automated intelligent image analysis. In Proc. of IEEE VIS. 191--198Google ScholarCross Ref
Index Terms
- ClusterTag: Interactive Visualization, Clustering and Tagging Tool for Big Image Collections
Recommendations
Application of Interactive Computer-Assisted Argument Extraction to Opinionated Social Media Texts
VINCI '18: Proceedings of the 11th International Symposium on Visual Information Communication and InteractionThe analysis of various opinions and arguments in textual data can be facilitated by automatic topic modeling methods; however, the exploration and interpretation of the resulting topics and terms may prove to be difficult to the analysts. Opinions, ...
Using social visualization to motivate social production
Special section on communities and media computingIn this paper we argue that social visualization can motivate contributors to social production projects, such as Wikipedia and open source development. As evidence, we present CodeSaw, a social visualization of open source software development that we ...
Image browsing on large high-resolution displays
MUM '17: Proceedings of the 16th International Conference on Mobile and Ubiquitous MultimediaVisual exploration of large image data sets is a common and widely performed task. With the ubiquity of cameras, personal image collections are growing. Even when working with large high-resolution displays, collections are typically too large to ...
Comments