Boosted kernel for image categorization
| Autor: | Alexis Lechervy, Frédéric Precioso, Philippe-Henri Gosselin |
|---|---|
| Přispěvatelé: | Multimedia Indexation and Data Integration (MIDI), Equipes Traitement de l'Information et Systèmes (ETIS - UMR 8051), Ecole Nationale Supérieure de l'Electronique et de ses Applications (ENSEA)-Centre National de la Recherche Scientifique (CNRS)-CY Cergy Paris Université (CY)-Ecole Nationale Supérieure de l'Electronique et de ses Applications (ENSEA)-Centre National de la Recherche Scientifique (CNRS)-CY Cergy Paris Université (CY), Laboratoire d'Informatique, Signaux, et Systèmes de Sophia-Antipolis (I3S) / Equipe KEIA, Scalable and Pervasive softwARe and Knowledge Systems (Laboratoire I3S - SPARKS), Laboratoire d'Informatique, Signaux, et Systèmes de Sophia Antipolis (I3S), Université Nice Sophia Antipolis (... - 2019) (UNS), COMUE Université Côte d'Azur (2015-2019) (COMUE UCA)-COMUE Université Côte d'Azur (2015-2019) (COMUE UCA)-Centre National de la Recherche Scientifique (CNRS)-Université Côte d'Azur (UCA)-Université Nice Sophia Antipolis (... - 2019) (UNS), COMUE Université Côte d'Azur (2015-2019) (COMUE UCA)-COMUE Université Côte d'Azur (2015-2019) (COMUE UCA)-Centre National de la Recherche Scientifique (CNRS)-Université Côte d'Azur (UCA)-Laboratoire d'Informatique, Signaux, et Systèmes de Sophia Antipolis (I3S), COMUE Université Côte d'Azur (2015-2019) (COMUE UCA)-COMUE Université Côte d'Azur (2015-2019) (COMUE UCA)-Centre National de la Recherche Scientifique (CNRS)-Université Côte d'Azur (UCA), Gosselin, Philippe-Henri |
| Jazyk: | angličtina |
| Rok vydání: | 2013 |
| Předmět: |
Graph kernel
Boosting (machine learning) Computer Networks and Communications Computer science Machine learning computer.software_genre Kernel (linear algebra) [STAT.ML]Statistics [stat]/Machine Learning [stat.ML] Polynomial kernel Media Technology Multiple kernel learning Training set business.industry Pattern recognition [STAT.ML] Statistics [stat]/Machine Learning [stat.ML] Support vector machine Kernel method Categorization Hardware and Architecture Kernel embedding of distributions Feature (computer vision) Kernel (statistics) Radial basis function kernel Embedding Artificial intelligence Tree kernel business computer Software |
| Zdroj: | Multimedia Tools and Applications Multimedia Tools and Applications, Springer Verlag, 2013 HAL |
| ISSN: | 1380-7501 1573-7721 |
| Popis: | International audience; Recent machine learning techniques have demonstrated their capability for identifying image categories using image features. Among these techniques, Support Vector Machines (SVM) present good results for example in Pascal Voc challenge 2011 [8], particularly when they are associated with a kernel function [28, 35]. However, nowadays image categorization task is very challenging owing to the sizes of benchmark datasets and the number of categories to be classified. In such a context, lot of effort has to be put in the design of the kernel functions and underlying semantic features. In the following of the paper we call semantic features the features describing the (semantic) content of an image. In this paper, we propose a framework to learn an effective kernel function using the Boosting paradigm to linearly combine weak kernels. We then use a SVM with this kernel to categorize image databases. More specifically, this method create embedding functions to map images in a Hilbert space where they are better classified. Furthermore, our algorithm benefits from boosting process to learn this kernel with a complexity linear with the size of the training set. Experiments are carried out on popular benchmarks and databases to show the properties and behavior of the proposed method. On the PASCAL VOC2006 database, we compare our method to simple early fusion, and on the Oxford Flowers databases we show that our method outperforms the best Multiple Kernel Learning (MKL) techniques of the literature. |
| Databáze: | OpenAIRE |
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