Feature Combination with Multi-Kernel Learning for Fine-Grained Visual Classification

Anelia Angelova, Alexandru Niculescu-Mizil

WACV 2014 pp. 241-246

doi:10.1109/WACV.2014.6836094 /wacv/2014/angelova2014wacv-feature/

Abstract

This paper addresses the problem of fine-grained recognition in which local, mid-level features are used for classification. We propose to use the Multi-Kernel Learning framework to learn the relative importance of the features and to select optimal features with regards to the classification performance, in a principled way. Our results show improved classification results on common benchmarks for fine-grained classification, as compared to the best prior state-of-the-art methods. The proposed learning-based combination method also improves the concatenation combination approach which has been the standard practice in combining features so far.

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Cite

Text

Angelova and Niculescu-Mizil. "Feature Combination with Multi-Kernel Learning for Fine-Grained Visual Classification." IEEE/CVF Winter Conference on Applications of Computer Vision, 2014. doi:10.1109/WACV.2014.6836094

Markdown

[Angelova and Niculescu-Mizil. "Feature Combination with Multi-Kernel Learning for Fine-Grained Visual Classification." IEEE/CVF Winter Conference on Applications of Computer Vision, 2014.](https://mlanthology.org/wacv/2014/angelova2014wacv-feature/) doi:10.1109/WACV.2014.6836094

BibTeX

@inproceedings{angelova2014wacv-feature,
  title     = {{Feature Combination with Multi-Kernel Learning for Fine-Grained Visual Classification}},
  author    = {Angelova, Anelia and Niculescu-Mizil, Alexandru},
  booktitle = {IEEE/CVF Winter Conference on Applications of Computer Vision},
  year      = {2014},
  pages     = {241-246},
  doi       = {10.1109/WACV.2014.6836094},
  url       = {https://mlanthology.org/wacv/2014/angelova2014wacv-feature/}
}