Towards a Rich and Dynamic Human Digital Memory in Egocentric Dataset
Khalid EL Asnaoui, Mohamed Ouhda, Brahim Aksasse, Mohammed Ouanan
Journal of E-Technology, Vol. 8, No. 4, 2017.
DOI: 
Link: http://www.dline.info/jet/fulltext/v8n4/jetv8n4_2.pdf
Abstract:
Memories have always been a considerable importance of a persons life and experiences. A digital human
memory as a field of study focuses on encapsulating this phenomenon, in digital form, during the thread of a lifetime. By
spreading hardware everywhere, massive amount of data is being generated together by people and the surrounding
environment. With all this demountable information available, successfully exploring, researching and collating, together, to
form a human digital memory, is a new challenge and requires novel and efficient algorithmic solutions. The main goal of this
work is going to propose a new method to automatically create rich and dynamic human digital memory in egocentric
dataset from the lifelogging images of a person. For this purpose, we will propose a technique using Convolutional Neural
Network (CNN) model. For validation, we will apply the proposed method on the Egocentric Dataset of University of
Barcelona (EDUB) of 4912 daily images acquired by four persons.