Recognition of Geometrical Shapes by Dictionary Learning
This work addresses shape recognition in computer vision, but it is incremental as it adapts an existing method to a new task.
The authors tackled the problem of shape recognition for geometrical shapes by applying dictionary learning, demonstrating that the choice of optimization method significantly impacts recognition quality, with experimental results suggesting it is a promising approach.
Dictionary learning is a versatile method to produce an overcomplete set of vectors, called atoms, to represent a given input with only a few atoms. In the literature, it has been used primarily for tasks that explore its powerful representation capabilities, such as for image reconstruction. In this work, we present a first approach to make dictionary learning work for shape recognition, considering specifically geometrical shapes. As we demonstrate, the choice of the underlying optimization method has a significant impact on recognition quality. Experimental results confirm that dictionary learning may be an interesting method for shape recognition tasks.