Name
Abstract | ||
|---|---|---|
Unknown landscape identification is the problem of identifying an unknown landscape from a set of already provided landscape images that are considered to be known. The aim of this work is to extract the intrinsic semantic of landscape images in order to automatically generalize concepts like a stadium, roads, a parking lot etc., and use this concept to identify unknown landscapes. This problem can be easily extended to many security applications. We propose two effective semi-supervised novelty detection approaches for the unknown landscape identification problem using Convolutional Neural Network (CNN) Transfer Learning. This is based on the use of pre-trained CNNs (i.e. already trained on large datasets) already containing general image knowledge that we transfer to our domain. Our best values of AUROC and Average Precision scores for the identification problem are 0.96 and 0.94, respectively. In addition, we statistically prove that our semi-supervised methods outperform the baseline.
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Year | DOI | Venue |
|---|---|---|
2018 | 10.5555/3382225.3382400 | ASONAM '18: International Conference on Advances in Social Networks Analysis and Mining
Barcelona
Spain
August, 2018 |
Field | DocType | ISBN |
Novelty detection,Task analysis,Computer science,Convolutional neural network,Support vector machine,Transfer of learning,Feature extraction,Artificial intelligence,Parameter identification problem,Machine learning,Mixture model | Conference | 978-1-5386-6051-5 |
Citations | PageRank | References |
0 | 0.34 | 0 |
Authors | ||
4 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Edoardo Serra | 1 | 5 | 4.87 |
| Ashish Sharma | 2 | 6 | 4.16 |
| Mikel Joaristi | 3 | 0 | 1.69 |
| Oxana Korzh | 4 | 0 | 0.68 |