Visual words for automated visual inspection of bulk materials

The inspection of bulk materials in mining, recycling and food-safety places strong requirements on the speed, accuracy and flexibility of automated visual inspection systems. State of the art methods utilize complex feature descriptors and off-the-shelve machine learning techniques. These methods achieve highly accurate results, but typically suffer in execution speed. Commercial systems, on the other hand, use simple features and classifiers to achieve great processing speed, but pay by a complicated intialization procedure and suboptimal classification accuracy. In this paper, we propose to bridge the gap between the two extremes by learning high level object representations that can be used with simple classifiers. For that, we adapt the well known bag of visual words method to use dense sampling and primitive features. The resulting descriptors are very fast to compute and invariant to scale and rotation. At the same time, the method is virtually parameter-free. This allows non-experts to initializate and operate sorting systems based on this approach. We evaluate our method on three food inspection applications. In all experiments we achieve highly accurate, sometimes nearly perfect classification. Comparison to a state of the art method shows that our approach is superior, beating it by a large margin.