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Multiple Kernel Learning for Visual Object Recognition: A Review.

Serhat S Bucak, Rong Jin, Anil K Jain

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    Summary

    Multiple Kernel Learning (MKL) enhances object recognition by effectively combining diverse feature sets. This study clarifies conflicting results, showing MKL

    Area of Science:

    • Computer Vision
    • Machine Learning
    • Pattern Recognition

    Background:

    • Multiple Kernel Learning (MKL) is a key technique for combining multiple feature representations in recognition tasks.
    • Existing studies on MKL for object recognition present conflicting findings regarding its effectiveness and computational efficiency.
    • Understanding the impact of experimental setups on MKL performance is crucial for practitioners.

    Purpose of the Study:

    • To systematically review state-of-the-art MKL formulations and algorithms.
    • To evaluate various MKL approaches for object recognition through extensive experiments.
    • To reconcile conflicting results in the literature by analyzing experimental setup variations.

    Main Methods:

    • Comprehensive review of MKL formulations and optimization algorithms.

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  • Extensive experimental evaluation of different MKL approaches on standard object recognition datasets.
  • Comparative analysis of MKL effectiveness against simple kernel combination methods.

    • Main Results:

    • MKL significantly outperforms simple kernel combination methods (e.g., best kernel selection, kernel averaging) when sufficient training data and feature types are available.
    • Conflicting results in prior studies are attributed to variations in experimental setups.
    • Specific MKL optimization algorithms, including sequential minimal optimization, semi-infinite programming, and level methods, demonstrate superior computational efficiency.

    Conclusions:

    • MKL is a highly effective strategy for object recognition, particularly when leveraging diverse feature sets.
    • The choice of experimental setup critically influences reported MKL performance.
    • Certain MKL algorithms offer a favorable balance of effectiveness and computational efficiency for practical applications.