Using Content-Based Image Retrieval to automatically assess day similarity in visual lifelogs

Today, we witness the appearance of many lifelogging cameras that are able to capture the life of a person wearing the camera, 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 similarity between daily visual data. To this purpose, we apply 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 4 persons with preliminary encouraging results.