Title | Transfer Learning based Natural Scene Classification for Scene Understanding by Intelligent Machines |
---|
Authors | Surendran, R., Anitha, J., Angelopoulou, A., Chaussalet, T.J., Kapetanios, D.E. and Jude Hemanth, D. |
---|
Type | Conference paper |
---|
Abstract | Scene classification carry out an imperative accountability in the current emerging field of automation. Traditional classification methods endure with tedious processing techniques. With the advent of CNN and deep learning models have greatly accelerated the job of scene classification. In our paper we have considered an area of application where the deep learning can be used to assist in the civil and military applications and aid in navigation. Current image classifications concentrate on the various available labeled datasets of various images. This work concentrates on classification of few scenes that contain pictures of people and places that are affected in the areas of flood. This aims at assisting the rescue officials at the need of natural calamities, disasters, military attacks etc. Proposed work explains a classifying system which can categorize the small scene dataset using transfer learning approach. We collected the pictures of scenes from sites and created a small dataset with different flood affected activities. We have utilized transfer learning model, RESNET in our proposed work which showed an accuracy of 88.88% for ResNet50 and 91.04% for ResNet101 and endow with a faster and economical revelation for the application involved. |
---|
Keywords | Scene Classification |
---|
| Image Classification |
---|
| Deep Learning |
---|
| Transfer Learning |
---|
| Res Net |
---|
Year | 2022 |
---|
Conference | International Conference on Computational Science ICCS 2022 |
---|
Publisher | Springer |
---|
Accepted author manuscript | File Access Level Open (open metadata and files) |
---|
Publisher's version | File Access Level Open (open metadata and files) |
---|
Publication dates |
---|
Published | 15 Jun 2022 |
---|
Journal | Lecture Notes in Computer Science |
---|
ISBN | 9783031087530 |
---|
Digital Object Identifier (DOI) | https://doi.org/10.1007/978-3-031-08754-7_6 |
---|
Web address (URL) of conference proceedings | https://www.iccs-meeting.org/iccs2022/ |
---|
Web address (URL) | https://www.iccs-meeting.org/iccs2022/ |
---|