Investigating Active Learning based on Dynamic Data Selection techniques for Image Classification

Abstract

This paper explores the efficacy of Active Learning (AL) techniques, specifically focusing on Dynamic Data Selection (DDS), for improving image classification tasks. AL is a machine learning paradigm that enables the automatic selection of the most informative data samples for annotation, thereby reducing the annotation burden and enhancing model performance. In this study, we investigate the integration of DDS techniques with AL strategies to iteratively select the most informative image samples for model training. We use a fine-tuned VGG16 as the underlying classification model due to their effectiveness in image analysis tasks. Our experimental evaluation involves comparing the performance of fine-tuned VGG16 trained with three AL-based DDS techniques on Arabic sign language dataset. We analyze various DDS strategies, including Random selection, Entropy-based selection, and margin selection to determine their impact on model accuracy and annotation efficiency. The results of our study demonstrate the effectiveness of margin selection method-based AL approach in improving the performance of recognition of 32 hand gestures for Arabic sign language (95.3 \%) while minimizing the annotation effort.