Boosted Metric Learning for Efficient Identity-Based Face Retrieval
| Autor: | Alexis Lechervy, Romain Negrel, Frédéric Jurie |
|---|---|
| Přispěvatelé: | Equipe Image - Laboratoire GREYC - UMR6072, Groupe de Recherche en Informatique, Image et Instrumentation de Caen (GREYC), Centre National de la Recherche Scientifique (CNRS)-École Nationale Supérieure d'Ingénieurs de Caen (ENSICAEN), Normandie Université (NU)-Normandie Université (NU)-Université de Caen Normandie (UNICAEN), Normandie Université (NU)-Centre National de la Recherche Scientifique (CNRS)-École Nationale Supérieure d'Ingénieurs de Caen (ENSICAEN), Normandie Université (NU) |
| Rok vydání: | 2015 |
| Předmět: |
Boosting (machine learning)
MLBoost business.industry Quadratic complexity metric learning Image processing Pattern recognition 02 engineering and technology Overfitting Machine learning computer.software_genre ML image processing Hierarchical clustering [INFO.INFO-LG]Computer Science [cs]/Machine Learning [cs.LG] Large face [INFO.INFO-TI]Computer Science [cs]/Image Processing [eess.IV] 0202 electrical engineering electronic engineering information engineering Hierarchical organization [INFO]Computer Science [cs] 020201 artificial intelligence & image processing Artificial intelligence business computer Mathematics |
| Zdroj: | BMVC British Machine Vision Conference 2015 proceedings 26th British Machine Vision Conference 26th British Machine Vision Conference, Sep 2015, Swansea, United Kingdom. ⟨10.5244/C.29.139⟩ |
| DOI: | 10.5244/c.29.139 |
| Popis: | International audience; This paper presents MLBoost, an efficient method for learning to compare face signatures , and shows its application to the hierarchical organization of large face databases. More precisely, the proposed metric learning (ML) algorithm is based on boosting so that the metric is learned iteratively by combining several weak metrics. Boosting allows our method to be free of any hyper-parameters (no cross-validation required) and to be robust with respect to overfitting. This MLBoost algorithm can be trained from constraints involving two pairs of vectors (quadruplets) with a quadratic complexity. The paper also shows how it can be included in a semi-supervised hierarchical clustering framework adapted to identity based face search. Our approach is validated on a benchmark relying on the Labelled Faces in the Wild (LFW) dataset supplemented with 1M face distractors. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
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