Knowing when you don't: Bag of visual words with reject option for automatic visual inspection of bulk materials

Visual inspection of bulk material is the thorough optical inspection of streams of granular material to assess their quality or to detect defective objects. Examples are found in mining (discovery of ores), recycling (sorting waste from reusable material) and food safety (detection of pathogens). In these applications, it is generally not feasible or even possible to provide an accurate and exhaustive training set of all the materials that can be encountered during the inspection. Instead, classification has to be performed in an open world setting, i.e., with the option to recognize and reject unknown objects. Despite the practical relevance, prior work on this topic is surprisingly sparse. Here, we present a method to augment bag of visual words object descriptors by an additional unknown word that encodes outliers. The method depends on only few parameters that have a clear interpretation and is suitable for the application in the field. We demonstrate the performance of our approach using two real-world datasets and compare it to a related method. The experiments show that our method significantly outperforms classification with a closed world assumption as well as the related method.