@conference {cAlsina, title = {An Interactive Lifelog Search Engine for LSC2018}, booktitle = {Lifelog Search Challenge workshop at ICMR2018}, year = {2018}, month = {06/2018}, publisher = {ACM}, organization = {ACM}, address = {Yokohama, Japan}, abstract = {

In this work, we describe an interactive lifelog search engine developed for the LSC 2018 search challenge at ACM ICMR 2018. The paper introduces the four-step process required to support lifelog search engines and describes the source data for the search engine as well as the approach to ranking chosen for the iterative search engine. Finally the interface used is introduced before we highlight the limits of the current prototype and suggest opportunities for future work.

}, doi = {10.1145/3210539.3210546}, url = {https://dl.acm.org/citation.cfm?id=3210546}, author = {Alsina, Adri{\`a} and Xavier Gir{\'o}-i-Nieto and Gurrin, Cathal} } @mastersthesis {xAlsina, title = {An interactive Lifelog Search Engine for LSC2018}, year = {2018}, abstract = {

This thesis consists on developing an interactive lifelog search engine for the LSC 2018 search\ challenge at ACM ICMR 2018. This search engine is created in order to browse for images from a given lifelog dataset and display them along with some written information related to them and four other images providing contextualization about the searched one.

First of all, the work makes an introduction to the relevance of this project. It introduces the\ reader to the main social problems affronted and the aim of our project to deal with them. Thus, go ahead with the scope of the project introducing to the main objectives fixed. Also, the work is gone by the actual state of the same kind of prototypes that already exist to let the reader see the differences that our project presents.

After the project approach is done, it begins a travel trough the methodology and creation\ process, going deep in the main aspects and the explanation of every election and decision, also remarking the limits of the current prototype.

Additionally, the project concludes with a result section where the system is tested with six users. They are asked to find three specific images using the search engine. This test is divided in two sections: first, a qualitative section where the user is asked to test the system and fill out a survey to see how comfortable it is for him. And a second section, more quantitative, where they value the speed of our system.

Finally, the project concludes going through the actual and future ethics of lifelogging in\ general and with a final conclusion further investigation and future improvement.

}, author = {Alsina, Adri{\`a}}, editor = {Gurrin, Cathal and Xavier Gir{\'o}-i-Nieto} } @article {aDimiccoli18, title = {Introduction to the special issue: Egocentric Vision and Lifelogging}, journal = {Journal of Visual Communication and Image Representation}, year = {2018}, month = {06/2018}, abstract = {

}, doi = {10.1016/j.jvcir.2018.06.010}, url = {https://www.sciencedirect.com/science/article/pii/S1047320318301354}, author = {Dimiccoli, M. and Gurrin, Cathal and Crandall, David and Xavier Gir{\'o}-i-Nieto and Radeva, Petia} } @conference {cLinardos, title = {Temporal Saliency Adaptation in Egocentric Videos}, booktitle = {ECCV 2018 Workshop on Egocentric Perception, Interaction and Computing}, year = {2018}, month = {07/2018}, publisher = {Extended abstract}, organization = {Extended abstract}, address = {Munich, Germany}, abstract = {

}, author = {Linardos, Panagiotis and Mohedano, Eva and Chert{\'o}, M{\`o}nica and Gurrin, Cathal and Xavier Gir{\'o}-i-Nieto} } @conference {cGurrina, title = {LTA 2017: The Second Workshop on Lifelogging Tools and Applications}, booktitle = {ACM Multimedia}, year = {2017}, month = {10/2017}, publisher = {ACM}, organization = {ACM}, address = {Mountain View, California USA}, abstract = {

The organisation of personal data is receiving increasing research attention due to the challenges we face in gathering, enriching, searching, and visualising such data. \ Given the increasing ease with which personal data being gathered by individuals, the concept of a lifelog digital library of rich multimedia and sensory content for every individual is fast becoming a reality. \ The LTA~2017 workshop aims to bring together academics and practitioners to discuss approaches to lifelog data analytics and applications; and to debate the opportunities and challenges for researchers in this new and challenging area.

}, doi = {10.1145/3123266.3132050}, author = {Gurrin, Cathal and Xavier Gir{\'o}-i-Nieto and Radeva, Petia and Dimiccoli, M. and Dang-Nguyen, Duc-Tien and Joho, H.} } @mastersthesis {xCarne-Herrera16, title = {Detect Snap Points in Egocentric Images with Physiological Signals}, year = {2016}, abstract = {

This project addresses a novel problem that has appears in the last years. The use of egocentric cameras, devices that takes images of what we see are growing and the main problem of this images are big data (because at the end of the day, we can have a thousand of images, some of them similar and sometimes with a bad quality or low information) and image retrieval (due to the big data, find a certain moment are very difficult and if we don{\textquoteright}t avoid that problem, the properties of egocentric images became useless).

This work have two objectives: the first one explore images that have physiological signals associated in order to allow us to add some physiological features for the retrieval instead of only base the retrieval in visual features as the actual state of the art. For this part we associate interesting images to the images that are memorable, so a correlation between memorability and physiological signals will be found. The second objective is to deal with the egocentric paradigm. Some recent works shows that machine learning algorithms that have been trained with human-taken images cannot be extended to egocentric images due to the image construction. Based on MIT (Massachusetts Institute of Technology) previous work I built a visual game that allows me to manually annotate the memorability of the images with a simple user interaction (the user don{\textquoteright}t know that he is annotating images during the game). From this game I have computed memorability score and I have obtain predicted scores from a convolutional neural network that MIT present in his work: MemNet. From both results I have compared the results in order to decide if the application of the algorithms on egocentric images is possible.

}, author = {Carn{\'e}-Herrera, Marc}, editor = {Gurrin, Cathal and Xavier Gir{\'o}-i-Nieto} } @article {xCarne-Herrera, title = {EgoMemNet: Visual Memorability Adaptation to Egocentric Images}, year = {2016}, month = {06/2016}, institution = {4th Workshop on Egocentric (First-Person) Vision, CVPR 2016}, address = {Las Vegas, NV, USA}, abstract = {

This work explores the adaptation of visual memorability\ prediction for photos intentionally captured by handheld\ cameras, to images passively captured from an egocentric\ point of view by wearable cameras. The estimation of a visual\ memorability score for an egocentric images is a valuable\ cue when filtering among the large amount of photos\ generated by wearable cameras. For this purpose, a new\ annotation tool and annotated dataset are presented, used\ to fine-tune a pre-trained convolutional neural network.

Extended abstract presented as poster in the\ 4th Workshop on Egocentric (First-Person) Vision,\ CVPR 2016.

}, author = {Carn{\'e}-Herrera, Marc and Xavier Gir{\'o}-i-Nieto and Gurrin, Cathal} } @mastersthesis {xCherto, title = {EgoMon Gaze and Video Dataset for Visual Saliency Prediction}, year = {2016}, abstract = {

This project focuses on the creation of a new type of egocentric (first person) vision dataset. For that purpose, the EgoMon Gaze \& Video Dataset is presented. This EgoMon dataset was recorded using the eye gaze tracking technology that studies the movement and position of the eyes. The Tobii glasses (wearable, eye tracker and head-mounted device) were the main tool used to record and extract the gaze data for this dataset. The dataset consists in 7 videos of 34 minutes each one of average, 13428 frames extracted from each video (with a frequency of 1 fps), and 7 files with the gaze data (fixations points of the wearer of the glasses) for each frame and video. The videos were recorded in the city of Dublin (Ireland) both indoor and outdoor. The generated dataset has been used to evaluate the performance of a state of art model for visual saliency prediction on egocentric video.

[Project page]

Grade: B (8.2/10.0)

EgoMon Gaze and Video Dataset for Visual Saliency Prediction from Xavier Giro
}, author = {Chert{\'o}, M{\`o}nica}, editor = {Gurrin, Cathal and Xavier Gir{\'o}-i-Nieto} } @conference {cGurrin, title = {LTA 2016 - The First Workshop on Lifelogging Tools and Applications}, booktitle = {ACM Multimedia}, year = {2016}, month = {10/2016}, publisher = {ACM}, organization = {ACM}, address = {Amsterdam, The Netherlands}, abstract = {

The organisation of personal data is receiving increasing research attention due to the challenges that are faced in gathering, enriching, searching and visualising this data. Given the increasing quantities of personal data being gathered by individuals, the concept of a lifelog digital library of rich multimedia and sensory content for every individual is fast becoming a reality. The LTA2016 lifelogging workshop at ACM MM 2016 aims to bring together academics and practitioners to discuss approaches to lifelog data analytics and the applications of same, and to debate the opportunities and challenges for researchers in this new and challenging area.

[Workshop web page]

[Workshop proceedings]

[UPCommons]

}, keywords = {lifelogging, Personal digital archives, Personal information management}, doi = {http://dx.doi.org/10.1145/2964284.2980534}, url = {http://lta2016.computing.dcu.ie/}, author = {Gurrin, Cathal and Xavier Gir{\'o}-i-Nieto and Radeva, Petia and Dimiccoli, M. and Johansen, H. and Joho, H. and Singh, Vivek K} } @mastersthesis {xCarne-Herreraa, title = {Visual Memorability for Egocentric Cameras}, year = {2016}, abstract = {

This project explores visual memorability of egocentric in different ways having three main\ contributions. The first and the main contribution of the project is a new tool visual\ memorability in egocentric images. This tool that consists in a web application that allows\ the annotation of the visual memorability associated to still images with an online game.\ The second contribution of this work is a convolutional neural network model for visual\ memorability prediction that adapts an off-the-shelf model to egocentric images. Moreover,\ a visualization study has been pursued to localize the regions of the images that are more\ memorable than others. With this maps a comparison with saliency maps and is explored.\ This part of the research opens a new branch in visual memorability that consists in use\ memorability maps for saliency prediction. Also the memorability of the images is related\ with a sentiment analysis applying a model that predicts that feature.\ The final contribution is related to join visual memorability of images with human\ behaviour and physical state, finding a relation between memory and some physiological\ signals as: heart rate, galvanic skin response and electroencephalographic signals.

Grade: A with honors (9.8/10.)

Visual Memorability for Egocentric Cameras from Xavier Giro
}, author = {Carn{\'e}-Herrera, Marc}, editor = {Gurrin, Cathal and Xavier Gir{\'o}-i-Nieto} } @conference {cMcGuinness, title = {Insight Centre for Data Analytics (DCU) at TRECVid 2014: Instance Search and Semantic Indexing Tasks}, booktitle = {2014 TRECVID Workshop}, year = {2014}, month = {11/2014}, publisher = {National Institute of Standards and Technology (NIST)}, organization = {National Institute of Standards and Technology (NIST)}, address = {Orlando, Florida (USA)}, abstract = {

Insight-DCU participated in the instance search (INS) and semantic indexing (SIN) tasks in 2014. Two very different approaches were submitted for instance search, one based on features extracted using pre-trained deep convolutional neural networks (CNNs), and another based on local SIFT features, large vocabulary visual bag-of-words aggregation, inverted index-based lookup, and geometric verification on the top-N retrieved results. Two interactive runs and two automatic runs were submitted, the best interactive runs achieved a mAP of 0.135 and the best automatic 0.12. Our semantic indexing runs were based also on using convolutional neural network features, and on Support Vector Machine classifiers with linear and RBF kernels. One run was submitted to the main task, two to the no annotation task, and one to the progress task. Data for the no-annotation task was gathered from Google Images and ImageNet. The main task run has achieved a mAP of 0.086, the best no-annotation runs had a close performance to the main run by achieving a mAP of 0.080, while the progress run had 0.043.

[2014 TREC Video Retrieval Evaluation Notebook Papers and Slides]

Poster

}, url = {http://hdl.handle.net/2117/24915}, author = {Kevin McGuinness and Mohedano, Eva and Zhang, ZhenXing and Hu, Feiyan and Albatal, Rami and Gurrin, Cathal and O{\textquoteright}Connor, N. and Smeaton, Alan F. and Amaia Salvador and Xavier Gir{\'o}-i-Nieto and Ventura, C.} }