Improving public datasets markings' quality using unsupervised refinement kernels
Anca Ioana Petre, Cosmin Ţoca, Carmen Pătraşcu, Mihai Ciuc · 2018
In recent years there has been an exponential growth in developing machine learning algorithms, focused on applications ranging from scene understanding, to the more standard object recognition and classification tasks. Although multiple approaches have been proposed for solving these issues, a common prerequisite is the existence of large datasets, which can be used both for training and testing purposes. We propose a semi-automatic annotation framework for object instances, which addresses the problems related to the big data paradigm in the context of object detection and pixel-level segmentation. The designed marking and learning workflow aims to be a cyclical process allowing iterative improvements of the marking architecture. Results of this processing chain are empirically validated on the COCO database.