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.

翻译：主动学习（Active Learning, AL）是一种用户交互式方法，旨在通过选择最关键样本进行标注以降低标注成本。尽管主动学习在图像分类任务中已得到广泛研究，但交互式图像检索这一特定场景却相对较少受到关注。该场景具有独特特征，包括开放集、类别不平衡的二分类问题，且初始标注样本极少。本文提出一种新颖的批处理模式主动学习框架GAL（贪心主动学习），能更好地适应此类应用。该框架采用一种新的样本选择获取函数，用于衡量每个未标注样本对分类器的影响。我们进一步将该策略嵌入贪心选择方法，以更充分地利用每批样本。我们使用线性（SVM）和非线性（MLP/高斯过程）分类器评估该框架。针对高斯过程分类器，我们证明了贪心近似策略的理论保证。最后，我们在多个基准数据集上评估了该方法在交互式基于内容图像检索任务中的性能，并证明了其相对于现有方法和常见基线的优越性。代码发布于 https://github.com/barleah/GreedyAL。