Activity Learning from Lifelogging Images

Date

2019-01-01

Author

Belli, Kader
Akbaş, Emre
Yazıcı, Adnan

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This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

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The analytics of lifelogging has generated great interest for data scientists because big and multi-dimensional data are generated as a result of lifelogging activities. In this paper, the NTCIR Lifelog dataset is used to learn activities from an image point of view. Minute definitions are classified into activity classes using images and annotations, which serve as a basis for various classification techniques, namely SVMs and convolutional neural network structures (CNN), for learning activities. The performance of the classification methods used in this study is evaluated and compared.

Subject Keywords

Lifelogging, Image classification, Text classification, SVM, CNN, ResNet-50, Machine learning, Deep learning

URI

https://hdl.handle.net/11511/43207

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Department of Computer Engineering, Conference / Seminar