Abstract
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.
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Acknowledgement
This study is supported in part by NU Faculty - development competitive research grants program, Nazarbayev University, Grant Number - 110119FD4543.
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Belli, K., Akbaş, E., Yazici, A. (2019). Activity Learning from Lifelogging Images. In: Rutkowski, L., Scherer, R., Korytkowski, M., Pedrycz, W., Tadeusiewicz, R., Zurada, J. (eds) Artificial Intelligence and Soft Computing. ICAISC 2019. Lecture Notes in Computer Science(), vol 11509. Springer, Cham. https://doi.org/10.1007/978-3-030-20915-5_30
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DOI: https://doi.org/10.1007/978-3-030-20915-5_30
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