The intermediate matching kernel for image local features

Sabri Boughorbel; Jean Philippe Tarel; Nozha Boujemaa

doi:10.1109/IJCNN.2005.1555970

The intermediate matching kernel for image local features

Sabri Boughorbel^*, Jean Philippe Tarel, Nozha Boujemaa

^*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

38 Citations (Scopus)

Abstract

We introduce the Intermediate Matching (IM) kernel for SVM-based object recognition. The IM kernel operates on a feature space of vector sets where each image is represented by a set of local features. Matching algorithms have proved to be efficient for such types of features. Nevertheless, kernelizing the matching for SVM does not lead to positive definite kernels. The IM kernel overcomes this drawback, as it mimics matching algorithms while being positive definite. The IM kernel introduces an intermediary set of so-called virtual local features. These select the pairs of local features to be matched. Comparisons with the Matching kernel shows that the IM kernels leads to similar performances.

Original language	English
Title of host publication	Proceedings of the International Joint Conference on Neural Networks, IJCNN 2005
Pages	889-894
Number of pages	6
DOIs	https://doi.org/10.1109/IJCNN.2005.1555970
Publication status	Published - 2005
Externally published	Yes
Event	International Joint Conference on Neural Networks, IJCNN 2005 - Montreal, QC, Canada Duration: 31 Jul 2005 → 4 Aug 2005

Publication series

Name	Proceedings of the International Joint Conference on Neural Networks
Volume	2

Conference

Conference	International Joint Conference on Neural Networks, IJCNN 2005
Country/Territory	Canada
City	Montreal, QC
Period	31/07/05 → 4/08/05

Access to Document

10.1109/IJCNN.2005.1555970

Cite this

@inproceedings{167a4bae80034689ba1de1091f6ecf40,

title = "The intermediate matching kernel for image local features",

abstract = "We introduce the Intermediate Matching (IM) kernel for SVM-based object recognition. The IM kernel operates on a feature space of vector sets where each image is represented by a set of local features. Matching algorithms have proved to be efficient for such types of features. Nevertheless, kernelizing the matching for SVM does not lead to positive definite kernels. The IM kernel overcomes this drawback, as it mimics matching algorithms while being positive definite. The IM kernel introduces an intermediary set of so-called virtual local features. These select the pairs of local features to be matched. Comparisons with the Matching kernel shows that the IM kernels leads to similar performances.",

author = "Sabri Boughorbel and Tarel, {Jean Philippe} and Nozha Boujemaa",

year = "2005",

doi = "10.1109/IJCNN.2005.1555970",

language = "English",

isbn = "0780390482",

series = "Proceedings of the International Joint Conference on Neural Networks",

pages = "889--894",

booktitle = "Proceedings of the International Joint Conference on Neural Networks, IJCNN 2005",

note = "International Joint Conference on Neural Networks, IJCNN 2005 ; Conference date: 31-07-2005 Through 04-08-2005",

}

Boughorbel, S, Tarel, JP & Boujemaa, N 2005, The intermediate matching kernel for image local features. in Proceedings of the International Joint Conference on Neural Networks, IJCNN 2005., 1555970, Proceedings of the International Joint Conference on Neural Networks, vol. 2, pp. 889-894, International Joint Conference on Neural Networks, IJCNN 2005, Montreal, QC, Canada, 31/07/05. https://doi.org/10.1109/IJCNN.2005.1555970

The intermediate matching kernel for image local features. / Boughorbel, Sabri; Tarel, Jean Philippe; Boujemaa, Nozha.
Proceedings of the International Joint Conference on Neural Networks, IJCNN 2005. 2005. p. 889-894 1555970 (Proceedings of the International Joint Conference on Neural Networks; Vol. 2).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

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AU - Tarel, Jean Philippe

AU - Boujemaa, Nozha

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N2 - We introduce the Intermediate Matching (IM) kernel for SVM-based object recognition. The IM kernel operates on a feature space of vector sets where each image is represented by a set of local features. Matching algorithms have proved to be efficient for such types of features. Nevertheless, kernelizing the matching for SVM does not lead to positive definite kernels. The IM kernel overcomes this drawback, as it mimics matching algorithms while being positive definite. The IM kernel introduces an intermediary set of so-called virtual local features. These select the pairs of local features to be matched. Comparisons with the Matching kernel shows that the IM kernels leads to similar performances.

AB - We introduce the Intermediate Matching (IM) kernel for SVM-based object recognition. The IM kernel operates on a feature space of vector sets where each image is represented by a set of local features. Matching algorithms have proved to be efficient for such types of features. Nevertheless, kernelizing the matching for SVM does not lead to positive definite kernels. The IM kernel overcomes this drawback, as it mimics matching algorithms while being positive definite. The IM kernel introduces an intermediary set of so-called virtual local features. These select the pairs of local features to be matched. Comparisons with the Matching kernel shows that the IM kernels leads to similar performances.

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DO - 10.1109/IJCNN.2005.1555970

M3 - Conference contribution

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SN - 0780390482

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T3 - Proceedings of the International Joint Conference on Neural Networks

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BT - Proceedings of the International Joint Conference on Neural Networks, IJCNN 2005

T2 - International Joint Conference on Neural Networks, IJCNN 2005

Y2 - 31 July 2005 through 4 August 2005

ER -

The intermediate matching kernel for image local features

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Conference

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