Title
Rapid Annotation Tool to Train Novel Concept Detectors with Active Learning
Author
de Boer, M.H.T.
Bouma, H.
Kruithof, M.C.
Joosten, B.
Publication year
2019
Abstract
Annotating a large set of images, especially with bounding boxes, is a tedious task. In this paper, we propose an intuitive image annotation tool. This tool not only allows (non-expert) users to annotate images with novel concepts, but is also able to achieve acceptable performance with a smaller number of annotated images. The tool can also propose detections on unannotated images, to provide faster annotation and insight in the performance of the system. The tool is based on a Single Shot Multi-box Detector (SSD) neural network with active learning, based on showing the images with high-confidence detections first, to have a fast verification and re-training. An experiment on simulated data shows that this active learning method can achieve higher performance in a shorter expected annotation time with a small number of images (less than 500). A small experiment on user annotated data shows that the annotation tool allows faster annotation compared to without the annotation tool
Subject
Image annotation
Concept localization
Deep learning
Active learning
To reference this document use:
http://resolver.tudelft.nl/uuid:795f906e-85f0-4445-aba3-4cd0c763f18f
TNO identifier
866281
Source
MMEDIA 2019: International Conference on Advances in Multimedia, Valencia 24-28 march 2019
Document type
conference paper