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High-Level Feature Detection with Forests of Fuzzy Decision Trees combined with the RankBoost

Christophe Marsala 1 Marcin Detyniecki 1 Nicolas Usunier 1 Massih-Reza Amini 1
1 MALIRE - Machine Learning and Information Retrieval
LIP6 - Laboratoire d'Informatique de Paris 6
Abstract : In this paper, we present the methodology we applied in our submission to the NIST TRECVID’2007 evaluation. We participated in the High-level Feature Extraction task. Our approach is based on the use of a Forest of Fuzzy Decision Trees combined with the RankBoost algorithm.
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https://hal.archives-ouvertes.fr/hal-01336167
Contributor : Lip6 Publications <>
Submitted on : Wednesday, June 22, 2016 - 4:37:08 PM
Last modification on : Friday, January 8, 2021 - 5:34:12 PM

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  • HAL Id : hal-01336167, version 1

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CNRS | UPMC | LIP6 | SORBONNE-UNIVERSITE | SU-SCIENCES

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Christophe Marsala, Marcin Detyniecki, Nicolas Usunier, Massih-Reza Amini. High-Level Feature Detection with Forests of Fuzzy Decision Trees combined with the RankBoost. TRECVID 2007 workshop participants notebook papers, Nov 2007, Gaithersburg, MD, United States. ⟨hal-01336167⟩

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