Abstract
Today, we witness the appearance of many lifelogging cameras that are able to capture the life of a person wearing the camera and which produce a large number of images everyday. Automatically characterizing the experience and extracting patterns of behavior of individuals from this huge collection of unlabeled and unstructured egocentric data present major challenges and require novel and efficient algorithmic solutions. The main goal of this work is to propose a new method to automatically assess day similarity from the lifelogging images of a person. We propose a technique to measure the similarity between images based on the Swain’s distance and generalize it to detect the similarity between daily visual data. To this purpose, we apply the dynamic time warping (DTW) combined with the Swain’s distance for final day similarity estimation. For validation, we apply our technique on the Egocentric Dataset of University of Barcelona (EDUB) of 4912 daily images acquired by four persons with preliminary encouraging results.
Methods
The search strategy was designed for high sensitivity over precision, to ensure that no relevant studies were lost. We performed a systematic review of the literature using academic databases (ACM, Scopus, etc.) focusing on themes of day similarity, automatically assess day similarity, assess day similarity on EDUB, and assess day similarity using visual lifelogs. The study included randomized controlled trials, cohort studies, and case-control studies published between 2006 and 2017.
Enhance Categorisation Of Multilevel High-Sensitivity Cardiovascular Biomarkers From Lateral Flow Immunoassay Images Via Neural Networks And Dynamic Time Warping
