Activity Learning from Lifelogging Images

Date

2019-01-01

Author

Belli, Kader
Akbaş, Emre
Yazıcı, Adnan

Metadata

Show full item record

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

Item Usage Stats

250
views

0
downloads

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

DOI

https://doi.org/10.1007/978-3-030-20915-5_30

Collections

Department of Computer Engineering, Conference / Seminar

Suggestions

OpenMETU
Core

Activity prediction from auto-captured lifelog images Belli, Kader; Akbaş, Emre; Department of Computer Engineering (2019) The analysis of lifelogging has generated great interest among data scientists because large-scale, multidimensional and multimodal data are generated as a result of lifelogging activities. In this study, we use the NTCIR Lifelog dataset where daily lives of two users are monitored for a total of 90 days, and archived as a set of minute-based records consisting of details like semantic location, body measurements, listening history, and user activity. In addition, images which are captured automatically by ...
Privacy-preserving horizontal federated learning methodology through a novel boosting-based federated random forest algorithm Gençtürk, Mert; Çiçekli, Fehime Nihan; Department of Computer Engineering (2023-1-04) In this thesis, a novel federated ensemble classification algorithm for horizontally partitioned data called Boosting-based Federated Random Forest (BOFRF) is proposed, which not only increases the predictive power of all participating sites, but also provides significantly high improvement on the predictive power of sites having unsuccessful local models. In this regard, a federated version of random forest, which is a well-known bagging algorithm, is implemented by adapting the idea of boosting to it. In ...
Deep Learning-Based Hybrid Approach for Phase Retrieval IŞIL, ÇAĞATAY; Öktem, Sevinç Figen; KOÇ, AYKUT (2019-06-24) We develop a phase retrieval algorithm that utilizes the hybrid-input-output (HIO) algorithm with a deep neural network (DNN). The DNN architecture, which is trained to remove the artifacts of HIO, is used iteratively with HIO to improve the reconstructions. The results demonstrate the effectiveness of the approach with little additional cost.
MODELLING OF KERNEL MACHINES BY INFINITE AND SEMI-INFINITE PROGRAMMING Ozogur-Akyuz, S.; Weber, Gerhard Wilhelm (2009-06-03) In Machine Learning (ML) algorithms, one of the crucial issues is the representation of the data. As the data become heterogeneous and large-scale, single kernel methods become insufficient to classify nonlinear data. The finite combinations of kernels are limited up to a finite choice. In order to overcome this discrepancy, we propose a novel method of "infinite" kernel combinations for learning problems with the help of infinite and semi-infinite programming regarding all elements in kernel space. Looking...
Mask Combination of Multi-Layer Graphs for Global Structure Inference Bayram, Eda; Thanou, Dorina; Vural, Elif; Frossard, Pascal (2020-01-01) Structure inference is an important task for network data processing and analysis in data science. In recent years, quite a few approaches have been developed to learn the graph structure underlying a set of observations captured in a data space. Although real-world data is often acquired in settings where relationships are influenced by a priori known rules, such domain knowledge is still not well exploited in structure inference problems. In this paper, we identify the structure of signals defined in a da...

Citation Formats

K. Belli, E. Akbaş, and A. Yazıcı, “Activity Learning from Lifelogging Images,” 2019, vol. 11509, Accessed: 00, 2020. [Online]. Available: https://hdl.handle.net/11511/43207.