Dynamic Feature Combination by Agreement for Image Classification

Image classification is a basic and important task in computer vision. Recently, various neural networks have been designed and proved to be very powerful models for image classification. It is natural for thinking of how to gather their strengths together, which refers to feature combination tasks....

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Bibliographic Details
Published inJournal of physics. Conference series Vol. 1631; no. 1; pp. 12151 - 12156
Main Authors Li, Chenghua, Wang, Wanguo, Liu, Linzhi, Liang, Tian
Format Journal Article
LanguageEnglish
Published Bristol IOP Publishing 01.09.2020
Subjects
Agreements
Algorithms
Classification
Computer vision
Image classification
Neural networks
Physics
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Summary:Image classification is a basic and important task in computer vision. Recently, various neural networks have been designed and proved to be very powerful models for image classification. It is natural for thinking of how to gather their strengths together, which refers to feature combination tasks. Traditional combination methods mainly focus on designing specific combination algorithms to achieve higher performance. However, few works consider how to utilize their agreement on a given target (for example, a specific class) to achieve better combinations. This paper presents a novel dynamic feature combination method (DFCA) for image classification problems based on the agreement of the individual features. DFCA promisingly takes the agreement of not only the commonalities, but also the individualities of different features by dynamically updating the weighting coefficients of given features using a routing module. Experiment and extensive analysis on CIFAR-10 prove the effectiveness and promising characteristics of the proposed method.
ISSN:1742-6588
1742-6596
DOI:10.1088/1742-6596/1631/1/012151