Object detection and recognition are classical problems in computer vision, but are still challenging without a priori knowledge of objects and with a limited user interaction. In this work, a semiautomatic system for visual object learning from video stream is presented. The system detects movable foreground objects relying on FAST interest points. Once a view of an object has been segmented, the system relies on ORB features to create its descriptor, store it and compare it with descriptors of previously seen views. To this end, a visual similarity function based on geometry consistency of the local features is used. The system groups together similar views of the same object into clusters relying on the transitivity of similarity among them. Each cluster identifies a 3D object and the system learn to autonomously recognize a particular view assessing its cluster membership. When ambiguities arise, the user is asked to validate the membership assignments. Experiments have demonstrated the ability of the system to group together unlabeled views, reducing the labeling work of the user.
Carrara, F., Falchi, F., & Gennaro, C. (2015). Semiautomatic learning of 3D objects from video streams. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 9371, pp. 217–228). Springer Verlag. https://doi.org/10.1007/978-3-319-25087-8_20