@inproceedings{4a006050ebd04f3c9c364788eb9e2ec8,
title = "Activity learning from lifelogging images",
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.",
keywords = "CNN, Deep learning, Image classification, Lifelogging, Machine learning, ResNet-50, SVM, Text classification",
author = "Kader Belli and Emre Akba{\c s} and Adnan Yazici",
note = "Funding Information: Acknowledgement. This study is supported in part by NU Faculty - development competitive research grants program, Nazarbayev University, Grant Number - 110119FD4543. Publisher Copyright: {\textcopyright} Springer Nature Switzerland AG 2019.; 18th International Conference on Artificial Intelligence and Soft Computing, ICAISC 2019 ; Conference date: 16-06-2019 Through 20-06-2019",
year = "2019",
month = jan,
day = "1",
doi = "10.1007/978-3-030-20915-5_30",
language = "English",
isbn = "9783030209148",
series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",
publisher = "Springer Verlag",
pages = "327--337",
editor = "Ryszard Tadeusiewicz and Witold Pedrycz and Zurada, {Jacek M.} and Leszek Rutkowski and Rafa{\l} Scherer and Marcin Korytkowski",
booktitle = "Artificial Intelligence and Soft Computing - 18th International Conference, ICAISC 2019, Proceedings",
address = "Germany",
}