LAUSR

The package design can transmit the value of a product to consumers visually and can therefore influence the consumers' decisions. The traditional package design is an open-loop process in which a design can only be evaluated after the products are sent to the market. Thus, the designers cannot refine the design without any helpful advice. In this paper, a robust robot image classification is proposed to help the designers to evaluate their package design and improve their design in a closed-loop process, which is essentially the establishment of a cyber-physical system for the package design. The robust robot image classification adopts the total variation regularization, which ensures that the proposed robot image classification can give the right answers even if it is trained by noisy labels. The robustness against noisy labels is emphasized here since the historical data set of package design evaluations may have some false labels that can be equivalently regarded as disturbed labels from the true labels by noises. To validate the effectiveness of the proposed robot image classification method, experimental data-based validations have been implemented. The results show that the proposed method exhibits much better accuracy in classification compared to the traditional training method when noisy labels are used for the training process.