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From Bits to Images: Inversion of Local Binary Descriptors
May 2014 (vol. 36 no. 5)
pp. 1-1
Alexandre Alahi, Stanford Vision Lab, Stanford University, Stanford, CA, USA
Pierre Vandergheynst, Signal Processing Labs (LTS2), Ecole Polytechnique Fédérale de Lausanne, Lausanne, Switzerland
Laurent Jacques, ICTEAM Institute, ELEN Department, Université Catholique de Louvain (UCL), Louvain-la-Neuve, Belgium
Emmanuel d'Angelo, , Advanced Silicon S.A., Lausanne, Switzerland
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.
Index Terms:
Image reconstruction,Vectors,Databases,Minimization,Mobile communication,Privacy,Benchmark testing,Reconstruction,Computer vision,Image Processing and Computer Vision,Feature representation,Representations,data structures,and transforms
Citation:
Alexandre Alahi, Pierre Vandergheynst, Laurent Jacques, Emmanuel d'Angelo, "From Bits to Images: Inversion of Local Binary Descriptors," IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 36, no. 5, pp. 1-1, May 2014, doi:10.1109/TPAMI.2013.228
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