The DOI matches its Crossref record; 9 of the 9 references it cites were found in a registry or an index.
Multiple Kernel Learning for Visual Object Recognition: A Review
Abstract
Multiple kernel learning (MKL) is a principled approach for selecting and combining kernels for a given recognition task. A number of studies have shown that MKL is a useful tool for object recognition, where each image is represented by multiple sets of features and MKL is applied to combine different feature sets. We review the state-of-the-art for MKL, including different formulations and algorithms for solving the related optimization problems, with the focus on their applications to object recognition. One dilemma faced by practitioners interested in using MKL for object recognition is that different studies often provide conflicting results about the effectiveness and efficiency of MKL. To resolve this, we conduct extensive experiments on standard datasets to evaluate various approaches to MKL for object recognition. We argue that the seemingly contradictory conclusions offered by studies are due to different experimental setups. The conclusions of our study are: (i) given a sufficient number of training examples and feature/kernel types, MKL is more effective for object recognition than simple kernel combination (e.g., choosing the best performing kernel or average of kernels); and (ii) among the various approaches proposed for MKL, the sequential minimal optimization, semi-infinite programming, and level method based ones are computationally most efficient.
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Cites
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Multimodal Machine Learning: A Survey and Taxonomy
Baltrusaitis, T., Ahuja, C., & Morency, L.-P. (2019) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2018.2798607 · Cited by 5,018
Learn to combine modalities in multimodal deep learning
Liu, K., Li, Y., Xu, N., & Natarajan, P. (2018) · arXiv · doi:10.48550/arxiv.1805.11730 · Cited by 136
Facial expression recognition in the wild based on multimodal texture features
Sun, B., Li, L., Zhou, G., & He, J. (2016) · Journal of Electronic Imaging · doi:10.1117/1.jei.25.6.061407 · Cited by 74
Wang, L., Wang, C., Li, C., Murai, T., Bai, Y., Song, Z., Zhang, S., Zhang, Q., Huang, Y., Bi, X., et al. (2025) · npj Digital Medicine · doi:10.1038/s41746-025-01933-3 · Cited by 33
“Multi-kernel learning allows the integration of multiple kernels 113 , each tailored to a speci fi c modality, to better synthesize heterogeneous data 114 .”
Donini, M., Monteiro, J. M., Pontil, M., Hahn, T., Fallgatter, A. J., Shawe-Taylor, J., & Mourão-Miranda, J. (2019) · NeuroImage · doi:10.1016/j.neuroimage.2019.01.053 · Cited by 19
Metadata
- Venue
- IEEE Transactions on Pattern Analysis and Machine Intelligence
- Published
- 2014-07
- Volume
- 36
- Issue
- 7
- Pages
- 1354-1369
- ISSN
- 0162-8828 (ISSN-L)
- 2160-9292
- OpenAlex
- W2112019442
- Cited by
- 274
- References
- 9
- FWCI
- 30.27
- Type
- journal article
- Language
- en
- Open access
- closed
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