Fresh food recognition using feature fusion

This paper presents a fresh food recognition system that utilizes the feature fusion extracted from food images captured from optical fibers embedded inside a chopping board. We exploit both local and global features including color, SURF and shape for image representation. In addition, we propose cost-based schemes for feature matching and the Borda count method for feature fusion. An experiment is conducted on our previous study´s dataset, which consists of 1,800 images of 12 food ingredients for evaluating the proposed method. The results demonstrate that the overall recognition accuracies can be achieved 86% precision and 83% recall, which is significantly improved from our previous work on food recognition.