Image Classification Using Multiple Convolutional Neural Networks on the Fashion-MNIST Dataset

As the elderly population grows, there is a need for caregivers, which may become unsustainable for society. In this situation, the demand for automated help increases. One of the solutions is service robotics, in which robots have automation and show significant promise in working with people. In particular, household settings and aged people's homes will need these robots to perform daily activities. Clothing manipulation is a daily activity and represents a challenging area for a robot. The detection and classification are key points for the manipulation of clothes. For this reason, in this paper, we proposed to study fashion image classification with four different neural network models to improve apparel image classification accuracy on the Fashion-MNIST dataset. The network models are tested with the highest accuracy with a Fashion-Product dataset and a customized dataset. The results show that one of our models, the Multiple Convolutional Neural Network including 15 convolutional layers (MCNN15), boosted the state of art accuracy, and it obtained a classification accuracy of 94.04% on the Fashion-MNIST dataset with respect to the literature. Moreover, MCNN15, with the Fashion-Product dataset and the household dataset, obtained 60% and 40% accuracy, respectively.

Automated summary

With the increasing elderly population, there is a growing need for caregivers, which may pose challenges for society. To address this, the use of service robotics is proposed as a solution, particularly in household settings and aged people's homes. This paper focuses on the manipulation of clothes, a daily activity, and proposes the study of fashion image classification using neural network models. The results show that the MCNN15 model achieved a classification accuracy of 94.04% on the Fashion-MNIST dataset, outperforming previous literature.