Unsupervised learning of categories from sets of partially matching image features for power line inspection robot

Object recognition and categorization are considered as fundamental steps in the vision based navigation for inspection robot as it must plan its behaviors based on various kinds of obstacles detected from the complex background. However, current approaches typically require some amount of supervision, which is viewed as a expensive burden and restricted to relatively small number of applications in practice. For this purpose, we present an computationally efficient approach that does not need supervision and is capable of learning object categories automatically from unlabeled images which are represented by an set of local features, and all sets are clustered according to their partial-match feature correspondences, which is done by a enhanced Spatial Pyramid Match algorithm (E-SPK). Then a graph-theoretic clustering method is applied to seek the primary grouping among the images. The consistent subsets within the groups are identified by inferring category templates. Given the input, the output of the approach is a partition of the images into a set of learned categories. We demonstrate this approach on a field experiment for a powerline inspection robot.