Active Learning via Classifier Impact and Greedy Selection for Interactive Image Retrieval

TL;DR

This paper introduces GAL, a novel batch-mode active learning framework for interactive image retrieval that uses a classifier impact-based acquisition function and greedy selection, showing superior performance on benchmarks.

Contribution

It proposes a new active learning method tailored for interactive image retrieval, incorporating impact-based sample selection and greedy batch selection, with theoretical guarantees for Gaussian Processes.

Findings

GAL outperforms existing methods on multiple benchmarks.

The impact-based acquisition function effectively identifies influential samples.

Theoretical guarantee provided for Gaussian Process classifiers.

Abstract

Active Learning (AL) is a user-interactive approach aimed at reducing annotation costs by selecting the most crucial examples to label. Although AL has been extensively studied for image classification tasks, the specific scenario of interactive image retrieval has received relatively little attention. This scenario presents unique characteristics, including an open-set and class-imbalanced binary classification, starting with very few labeled samples. We introduce a novel batch-mode Active Learning framework named GAL (Greedy Active Learning) that better copes with this application. It incorporates a new acquisition function for sample selection that measures the impact of each unlabeled sample on the classifier. We further embed this strategy in a greedy selection approach, better exploiting the samples within each batch. We evaluate our framework with both linear (SVM) and non-linear MLP/Gaussian Process classifiers. For the Gaussian Process case, we show a theoretical guarantee on the greedy approximation. Finally, we assess our performance for the interactive content-based image retrieval task on several benchmarks and demonstrate its superiority over existing approaches and common baselines. Code is available at https://github.com/barleah/GreedyAL.