  Local Binary Descriptors are becoming more and more popular for image
matching tasks, especially when going mobile. While they are extensively
studied in this context, their ability to carry enough information in order to
infer the original image is seldom addressed.
  In this work, we leverage an inverse problem approach to show that it is
possible to directly reconstruct the image content from Local Binary
Descriptors. This process relies on very broad assumptions besides the
knowledge of the pattern of the descriptor at hand. This generalizes previous
results that required either a prior learning database or non-binarized
features.
  Furthermore, our reconstruction scheme reveals differences in the way
different Local Binary Descriptors capture and encode image information. Hence,
the potential applications of our work are multiple, ranging from privacy
issues caused by eavesdropping image keypoints streamed by mobile devices to
the design of better descriptors through the visualization and the analysis of
their geometric content.
