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Enhancing the detection of concepts for visual lifelogs using contexts instead of ontologies

Wang, Peng, Smeaton, Alan F. orcid logoORCID: 0000-0003-1028-8389, Zhang, Yuchao and Deng, Bo (2014) Enhancing the detection of concepts for visual lifelogs using contexts instead of ontologies. In: International Workshop on the Visualisation of Heterogeneous Multimedia Content, VisHMC, In connection with the ICME Conference, 14 Jul 2014, Chengdu, China.

Automatic detection of semantic concepts in visual media is typically achieved by an automatic mapping from low-level features to higher level semantics and progress in automatic detection within narrow domains has now reached a satisfactory performance level. In visual lifelogging, part of the quantified-self movement, wearable cameras can automatically record most aspects of daily living. The resulting images have a diversity of everyday concepts which severely degrades the performance of concept detection. In this paper, we present an algorithm based on non-negative matrix refactorization which exploits inherent relationships between everyday concepts in domains where context is more prevalent, such as lifelogging. Results for initial concept detection are factorized and adjusted according to their patterns of appearance, and absence. In comparison to using an ontology to enhance concept detection, we use underlying contextual semantics to improve overall detection performance. Results are demonstrated in experiments to show the efficacy of our algorithm.
Item Type:Conference or Workshop Item (Paper)
Event Type:Workshop
Additional Information:alan.smeaton@dcu.ie
Uncontrolled Keywords:Visual lifelogging; Concept detection; Non-negative matrix factorization; Concept semantics
Subjects:Computer Science > Lifelog
Computer Science > Image processing
DCU Faculties and Centres:Research Institutes and Centres > INSIGHT Centre for Data Analytics
DCU Faculties and Schools > Faculty of Engineering and Computing > School of Computing
Official URL:http://vishmc2014.dai-labor.de/
Use License:This item is licensed under a Creative Commons Attribution-NonCommercial-Share Alike 3.0 License. View License
Funders:Science Foundation Ireland
ID Code:20049
Deposited On:17 Jul 2014 13:45 by Alan Smeaton . Last Modified 31 Oct 2018 11:56

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