Beyond bits: Reconstructing images from Local Binary Descriptors

Local Binary Descriptors (LBDs) are good at matching image parts, but how much information is actually carried? Surprisingly, this question is usually ignored and replaced by a comparison of matching performances. In this paper, we directly address it by trying to reconstruct plausible images from different LBDs such as BRIEF [4] and FREAK [1]. Using an inverse problem framework, we show that this task is achievable with only the information in the descriptors, excluding the need of additional data. Hence, our results represent a novel justification for the performance of LBDs. Furthermore, since plausible images can be inferred using only these simple measurements, this emphasizes the concerns about privacy and secrecy of image keypoints raised by [12], that could have an important impact on public applications of image matching.

[1]  David G. Lowe,et al.  Distinctive Image Features from Scale-Invariant Keypoints , 2004, International Journal of Computer Vision.

[2]  G LoweDavid,et al.  Distinctive Image Features from Scale-Invariant Keypoints , 2004 .

[3]  Pierre Vandergheynst,et al.  FREAK: Fast Retina Keypoint , 2012, 2012 IEEE Conference on Computer Vision and Pattern Recognition.

[4]  Laurent Jacques,et al.  Robust 1-Bit Compressive Sensing via Binary Stable Embeddings of Sparse Vectors , 2011, IEEE Transactions on Information Theory.

[5]  Cordelia Schmid,et al.  A Performance Evaluation of Local Descriptors , 2005, IEEE Trans. Pattern Anal. Mach. Intell..

[6]  M. Nikolova.  An Algorithm for Total Variation Minimization and Applications , 2004 .

[7]  Patrick Pérez,et al.  Reconstructing an image from its local descriptors , 2011, CVPR 2011.

[8]  Panu Turcot,et al.  Better matching with fewer features: The selection of useful features in large database recognition problems , 2009, 2009 IEEE 12th International Conference on Computer Vision Workshops, ICCV Workshops.

[9]  Antonin Chambolle,et al.  A First-Order Primal-Dual Algorithm for Convex Problems with Applications to Imaging , 2011, Journal of Mathematical Imaging and Vision.

[10]  Tom Drummond,et al.  Machine Learning for High-Speed Corner Detection , 2006, ECCV.

[11]  Vincent Lepetit,et al.  BRIEF: Binary Robust Independent Elementary Features , 2010, ECCV.

[12]  Christopher Hunt,et al.  Notes on the OpenSURF Library , 2009 .

[13]  Jiebo Luo,et al.  Interactively Co-segmentating Topically Related Images with Intelligent Scribble Guidance , 2011, International Journal of Computer Vision.