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Weakly supervised learning from scale invariant feature transform keypoints: an approach combining fast eigendecompostion, regularization, and diffusion on graphs

Youssef Chahir, Abderraouf Bouziane, Messaoud Mostefai, Adnan Al Alwani
<span title="2014-01-21">2014</span> <i title="SPIE-Intl Soc Optical Eng"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/2emxxxtvbjco7e2f75dok676dy" style="color: black;">Journal of Electronic Imaging (JEI)</a> </i> &nbsp;
We propose a unified approach to propagate knowledge into a high-dimensional space from a small informative set, in this case, scale invariant feature transform (SIFT) features.  ...  Finally, we describe our algorithm for multilabel diffusion on graph. Theoretical analysis of the algorithm is developed along with the corresponding connections with other methods.  ...  We also would like the thank Professor Abder Elmoataz for discussions about the discrete p-regularization and its constructive remarks.  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1117/1.jei.23.1.013009">doi:10.1117/1.jei.23.1.013009</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/k6vplew62nbtnhytg5d5bhe4re">fatcat:k6vplew62nbtnhytg5d5bhe4re</a> </span>
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