You are here

Insight Centre for Data Analytics (DCU) at TRECVid 2014: Instance Search and Semantic Indexing Tasks

Authors: 

Kevin McGuinness, Eva Mohedano, ZhenXing Zhang, Feiyan Hu, Rami Albatal, Cathal Gurrin, Noel O'Connor, Alan Smeaton, Amaia Salvador, Xavier Giro ́-i-Nieto, Carles Ventura

Publication Type: 
Refereed Conference Meeting Proceeding
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.
Conference Name: 
TRECVid 2014 Workshop
Proceedings: 
TRECVid 2014
Digital Object Identifer (DOI): 
1
Publication Date: 
08/11/2014
Conference Location: 
United States of America
Research Group: 
Institution: 
Dublin City University (DCU)
Open access repository: 
Yes