@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 {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.} }