Automated example selection for iterative training of annotation networks

Ugur Berk Sahin; Caglar Kavak; Yoldas Ataseven; Anıl Turker

doi:10.1117/12.2636013

28 October 2022 Automated example selection for iterative training of annotation networks

Ugur Berk Sahin, Caglar Kavak, Yoldas Ataseven, Anıl Turker

Proceedings Volume 12276, Artificial Intelligence and Machine Learning in Defense Applications IV; 1227605 (2022) https://doi.org/10.1117/12.2636013
Event: SPIE Security + Defence, 2022, Berlin, Germany

Abstract

Data annotation is a time-consuming, labor-intensive step in supervised learning, mainly for detection and classification. Most of the time, human effort for annotation is required to obtain an accurately labeled dataset, which is time-consuming and sometimes impossible, especially for large datasets. Most of the novel methods use various networks to annotate the data. However, numerous hand-labeled data are still required for those methods. In order to solve this problem, we propose a method to make the process as human-independent as possible while preserving the annotation performance. The proposed method is applicable to datasets, for which the majority of the frames/images contain a single object (or a known number, ”n”, of objects). The method starts with an initial annotation network that is trained with a small amount of labeled data, %10 of the total training set, and then it continues iteratively. We use the annotation network to select the subset of the training set that is to be hand-labeled for the next iteration. This way, examples that are more likely to improve the annotation network can be selected. The total number of necessary hand-labeled images is dependent on the specific problem. We observed that when the proposed approach was used rather than annotating all the images, manually annotating approximately %25 of the dataset was sufficient. This percentage can vary according to the complexity and the type of the annotation network, as well as the dataset content. Our method can be used with existing (semi) automatic annotation tools.

Conference Presentation

Citation Download Citation

Ugur Berk Sahin, Caglar Kavak, Yoldas Ataseven, and Anıl Turker "Automated example selection for iterative training of annotation networks", Proc. SPIE 12276, Artificial Intelligence and Machine Learning in Defense Applications IV, 1227605 (28 October 2022); https://doi.org/10.1117/12.2636013

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