Community-driven hierarchical fusion of numerous classifiers: Application to video semantic indexing

Herve Bredin
<span title="">2012</span> <i title="IEEE"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/rc5jnc4ldvhs3dswicq5wk3vsq" style="color: black;">2012 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)</a> </i> &nbsp;
We deal with the issue of combining dozens of classifiers into a better one. Our first contribution is the introduction of the notion of communities of classifiers. We build a complete graph with one node per classifier and edges weighted by a measure of similarity between connected classifiers. The resulting community structure is uncovered from this graph using the state-of-the-art Louvain algorithm. Our second contribution is a hierarchical fusion approach driven by these communities. First,
more &raquo; ... intra-community fusion results in one classifier per community. Then, inter-community fusion takes advantage of their complementarity to achieve much better classification performance. Application to the combination of 90 classifiers in the framework of TRECVid 2010 Semantic Indexing task shows a 30% increase in performance relative to a baseline flat fusion.
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1109/icassp.2012.6288381">doi:10.1109/icassp.2012.6288381</a> <a target="_blank" rel="external noopener" href="https://dblp.org/rec/conf/icassp/Bredin12.html">dblp:conf/icassp/Bredin12</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/hkaxesu7jffexfbz5lawokawtm">fatcat:hkaxesu7jffexfbz5lawokawtm</a> </span>
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