Evaluation of Weakly Supervised Learning Paradigms on Automatic Visual Inspection

In pattern recognition supervised learning algorithms has been used to model the relationships among the characteristics of a group of objects (or patterns) and their class labels. This model is build based on a set of training images that have been labelled by an expert in a specific problem domain. In general, the labelling process consists in defining the object's class label as well as demarcating the precise part of the image in which it is located the object of interest. In many computer vision applications, for example in automatic visual inspection, the labelling process may be a laborious task which requires an extensive work, it could even become impractical when a large number of training images are needed to train the pattern recognition system. This limitation has led to the development of new learning algorithms that allow some ambiguity or "weakness" in the way class labels are assigned. These algorithms are known as weakly supervised learning algorithms. In this paper we present an evaluation of different weakly supervised learning paradigms grouped according to the representation of the object of interest (in one or several characteristic vectors) associated to its class label (single or multiple label). We perform different experiments in the automatic visual inspection context, using a benchmark dataset of texture images with artificial defects. We test different weakly supervised learning algorithms. Results show that representing an object using multiple instances, presents better results in the identification of defective texture image