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SEMANTIC POOLING FOR IMAGE CATEGORIZATION USING MULTIPLE KERNEL LEARNING

Thibaut Durand 1, 2 David Picard 1 Nicolas Thome 2 Matthieu Cord 2
1 MIDI - Multimedia Indexation and Data Integration
ETIS - UMR 8051 - Equipes Traitement de l'Information et Systèmes
2 MLIA - Machine Learning and Information Access
LIP6 - Laboratoire d'Informatique de Paris 6
Abstract : In this paper, we propose a new method for taking into ac-count the spatial information in image categorization. More specifically, we remove the loss of spatial information in Bag of Words related methods by computing the image signature over specific regions selected by object detectors. We propose to select the detectors using Multiple Kernel Learning tech-niques. We carry out experiments on the well known VOC 2007 dataset, and show our semantic pooling obtains promis-ing results.
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https://hal.archives-ouvertes.fr/hal-01077046
Contributor : Thibaut Durand <>
Submitted on : Thursday, October 23, 2014 - 4:57:43 PM
Last modification on : Thursday, March 5, 2020 - 4:25:50 PM

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  • HAL Id : hal-01077046, version 1

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Thibaut Durand, David Picard, Nicolas Thome, Matthieu Cord. SEMANTIC POOLING FOR IMAGE CATEGORIZATION USING MULTIPLE KERNEL LEARNING. IEEE International Conference on Image Processing, Oct 2014, Paris, France. ⟨hal-01077046⟩

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