Extended Coding and Pooling in the HMAX Model

Christian Theriault Nicolas Thome 1 Matthieu Cord 1
1 MLIA - Machine Learning and Information Access
LIP6 - Laboratoire d'Informatique de Paris 6
Abstract : This paper presents an extension of the HMAX model, a neural network model for image classification. The HMAX model can be described as a four-level architecture, with the first level consisting of multiscale and multiorientation local filters. We introduce two main contributions to this model. First, we improve the way the local filters at the first level are integrated into more complex filters at the last level, providing a flexible description of object regions and combining local information of multiple scales and orientations. These new filters are discriminative and yet invariant, two key aspects of visual classification. We evaluate their discriminative power and their level of invariance to geometrical transformations on a synthetic image set. Second, we introduce a multiresolution spatial pooling. This pooling encodes both local and global spatial information to produce discriminative image signatures. Classification results are reported on three image data sets: Caltech101, Caltech256, and fifteen scenes. We show significant improvements over previous architectures using a similar framework.
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IEEE Transactions on Image Processing, Institute of Electrical and Electronics Engineers, 2013, 22 (2), pp.764-777. 〈10.1109/TIP.2012.2222900〉
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https://hal.archives-ouvertes.fr/hal-01185467
Contributeur : Lip6 Publications <>
Soumis le : jeudi 20 août 2015 - 11:38:45
Dernière modification le : vendredi 14 décembre 2018 - 01:25:11

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Christian Theriault, Nicolas Thome, Matthieu Cord. Extended Coding and Pooling in the HMAX Model. IEEE Transactions on Image Processing, Institute of Electrical and Electronics Engineers, 2013, 22 (2), pp.764-777. 〈10.1109/TIP.2012.2222900〉. 〈hal-01185467〉

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