  A technique named Feature Learning from Image Markers (FLIM) was recently
proposed to estimate convolutional filters, with no backpropagation, from
strokes drawn by a user on very few images (e.g., 1-3) per class, and
demonstrated for coconut-tree image classification. This paper extends FLIM for
fully connected layers and demonstrates it on different image classification
problems. The work evaluates marker selection from multiple users and the
impact of adding a fully connected layer. The results show that FLIM-based
convolutional neural networks can outperform the same architecture trained from
scratch by backpropagation.
