An automated method of creating lunchbox image datasets using a novel clustering algorithm

Meal assistance robots have been developed because people with upper limb disabilities have difficulty in eating by themselves. We develop a robot to automatically select and assist food by machine learning to operate more easily. This machine learning requires the creation of high-quality datasets for each type of food. In this paper, we propose the automatic improved method by using Density-Based Spatial Clustering of Applications with Noise repetitively to remove noisy images in the dataset. Experimental results show that the percentage of noise images in the dataset was reduced by 20%. In this way, we hope that the accuracy of automatic selection implemented in meal assistance robot is improved.