In this paper, we propose a No-Reference Image Quality Assessment (NRIQA) guided cut-off point selection (CPS) strategy to enhance the performance of a fine-grained classification system. Scores given by existing NRIQA methods on the same image may vary and not be as independent of natural image augmentations as expected, which weakens their connection and explainability to fine-grained image classification. Taking the three most commonly adopted image augmentation configurations -- cropping, rotating, and blurring -- as the entry point, we formulate a two-step mechanism for selecting the most discriminative subset from a given image dataset by considering both the confidence of model predictions and the density distribution of image qualities over several NRIQA methods. Concretely, the cut-off points yielded by those methods are aggregated via majority voting to inform the process of image subset selection. The efficacy and efficiency of such a mechanism have been confirmed by comparing the models being trained on high-quality images against a combination of high- and low-quality ones, with a range of 0.7% to 4.2% improvement on a commercial product dataset in terms of mean accuracy through four deep neural classifiers. The robustness of the mechanism has been proven by the observations that all the selected high-quality images can work jointly with 70% low-quality images with 1.3% of classification precision sacrificed when using ResNet34 in an ablation study.

翻译：本文提出了一种基于无参考图像质量评估（NRIQA）的截断点选择（CPS）策略，以提升细粒度分类系统的性能。现有NRIQA方法对同一图像给出的评分可能存在差异，且未如预期般独立于自然图像增强操作，这削弱了其与细粒度图像分类的关联性与可解释性。以三种最常用的图像增强配置——裁剪、旋转和模糊——为切入点，我们设计了一种两步机制，通过综合考虑模型预测的置信度与多种NRIQA方法下的图像质量密度分布，从给定图像数据集中筛选最具判别性的子集。具体而言，通过多数投票聚合各NRIQA方法生成的截断点，以指导图像子集选择过程。通过对比在高质量图像及高低质量混合图像上训练的模型，该机制的有效性与高效性得到验证：在四个深度神经分类器对商业产品数据集的测试中，平均准确率提升0.7%至4.2%。消融实验表明，所有选定的高质量图像可与70%的低质量图像协同工作，在使用ResNet34时仅牺牲1.3%的分类精度，从而证明了该机制的鲁棒性。