Image operator learning coupled with CNN classification and its application to staff line removal
This work addresses a specific bottleneck in image processing for music score analysis, offering an incremental improvement over existing techniques.
The paper tackled the limitation of window size in image operator learning by using convolutional neural networks (CNNs) to model pixel-wise local functions, and results showed that the CNN-based solution outperformed previous methods in staff-line removal from music score images.
Many image transformations can be modeled by image operators that are characterized by pixel-wise local functions defined on a finite support window. In image operator learning, these functions are estimated from training data using machine learning techniques. Input size is usually a critical issue when using learning algorithms, and it limits the size of practicable windows. We propose the use of convolutional neural networks (CNNs) to overcome this limitation. The problem of removing staff-lines in music score images is chosen to evaluate the effects of window and convolutional mask sizes on the learned image operator performance. Results show that the CNN based solution outperforms previous ones obtained using conventional learning algorithms or heuristic algorithms, indicating the potential of CNNs as base classifiers in image operator learning. The implementations will be made available on the TRIOSlib project site.