Name
Abstract | ||
|---|---|---|
Recent advances in lifelogging technologies, and in particular, in the field of wearable cameras, have made possible to capture continuously our daily life from a first-person point of view and in a free-hand fashion. However, given the huge amount of images captured and the rate to which they increase (up to 2000 images per day), there is a strong need for efficient and scalable indexing and retrieval systems over egocentric images. To cope with those requirements, we develop a full Content-Based Image Retrieval system based on Convolutional Neural Network (CNN) features. We use egocentric images to create a Lucene index with off-the-shelf features extracted from a pre-trained CNN. Finally, we provide a web-based prototype for egocentric image search and retrieval and tested its performances on the EDUB egocentric dataset. |
Year | DOI | Venue |
|---|---|---|
2016 | 10.3233/978-1-61499-696-5-71 | Frontiers in Artificial Intelligence and Applications |
Keywords | Field | DocType |
Egocentric vision,Lifelog,Content-Based Image Retrieval,CNN | Pattern recognition,Convolutional neural network,Computer science,Image retrieval,Artificial intelligence | Conference |
Volume | ISSN | Citations |
288 | 0922-6389 | 0 |
PageRank | References | Authors |
0.34 | 0 | 3 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Gabriel Oliveira-Barra | 1 | 2 | 1.06 |
| Mariella Dimiccoli | 2 | 89 | 18.29 |
| Petia Radeva | 3 | 1684 | 153.53 |