Multiple Kernel Learning via Distance Metric Learning for Interactive Image Retrieval
In this paper we formulate multiple kernel learning (MKL) as a distance metric learning (DML) problem. More specifically, we learn a linear combination of a set of base kernels by optimising two objective functions that are commonly used in distance metric learning. We first propose a global version...
| Main Authors: | , , |
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| Format: | Conference Object |
| Language: | English |
| Published: |
Springer
2011
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| Subjects: | |
| Online Access: | http://epubs.surrey.ac.uk/733275/1/MCS11.pdf http://epubs.surrey.ac.uk/733275/2/SRI_deposit_agreement.pdf https://doi.org/10.1007/978-3-642-21557-5_17 |
| Summary: | In this paper we formulate multiple kernel learning (MKL) as a distance metric learning (DML) problem. More specifically, we learn a linear combination of a set of base kernels by optimising two objective functions that are commonly used in distance metric learning. We first propose a global version of such an MKL via DML scheme, then a localised version. We argue that the localised version not only yields better performance than the global version, but also fits naturally into the framework of example based retrieval and relevance feedback. Finally the usefulness of the proposed schemes are verified through experiments on two image retrieval datasets. |
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