Convolutional neural networks (CNNs) apply well with food image recognition due to the ability to learn discriminative visual features. Nevertheless, recognizing distorted images is challenging for existing CNNs. Hence, the study modelled a generalized specialist approach to train a quality resilient ensemble. The approach aids the models in the ensemble framework retain general skills of recognizing clean images and shallow skills of classifying noisy images with one deep expertise area on a particular distortion. Subsequently, a novel data augmentation random quality mixup (RQMixUp) is combined with snapshot ensembling to train G-Specialist. During each training cycle of G-Specialist, a model is fine-tuned on the synthetic images generated by RQMixup, intermixing clean and distorted images of a particular distortion at a randomly chosen level. Resultantly, each snapshot in the ensemble gained expertise on several distortion levels, with shallow skills on other quality distortions. Next, the filter outputs from diverse experts were fused for higher accuracy. The learning process has no additional cost due to a single training process to train experts, compatible with a wide range of supervised CNNs for transfer learning. Finally, the experimental analysis on three real-world food and a Malaysian food database showed significant improvement for distorted images with competitive classification performance on pristine food images.
翻译:由于有能力学习歧视性视觉特征,革命神经网络(CNNs)与食品图像识别相适应。然而,承认扭曲的图像对于现有的CNN来说具有挑战性。因此,研究模拟了一种通用专家方法,以训练高质量的抗敏合体。该方法帮助混合框架中的模型保留了认识清洁图像的一般技能,以及将噪音图像分类的浅技能,并用一个深层的专门知识领域对特定扭曲进行分类。随后,新颖的数据增强随机质量混杂(RQMixUP)与对G-专家进行训练的快照组合。在G-专家的每个培训周期中,都对由RQMixup制作的合成图像、混合清洁和扭曲的图像进行微调。因此,组合中的每张图像在几个扭曲层次上都获得了专业知识,而其他质量扭曲的技巧则比较浅。随后,不同专家的过滤结果被结合到更高的准确度。学习过程没有增加费用,因为培训专家的单项培训过程,与一系列受监管的食品图像相兼容,混合清洁和扭曲的图像被随机,最后,在监控的食品上展示了全球的图像。