Being able to extract effective features from different images is very important for image classification, but it is challenging due to high variations across images. By integrating existing well-developed feature descriptors into learning algorithms, it is possible to automatically extract informative high-level features for image classification. As a learning algorithm with a flexible representation and good global search ability, genetic programming can achieve this. In this paper, a new genetic programming-based feature learning approach is developed to automatically select and combine five existing well-developed descriptors to extract high-level features for image classification. The new approach can automatically learn various numbers of global and/or local features from different types of images. The results show that the new approach achieves significantly better classification performance in almost all the comparisons on eight data sets of varying difficulty. Further analysis reveals the effectiveness of the new approach to finding the most effective feature descriptors or combinations of them to extract discriminative features for different classification tasks.

中文翻译：

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使用遗传编程和图像描述符进行图像分类的有效特征学习方法 [研究前沿]

能够从不同图像中提取有效特征对于图像分类非常重要，但由于图像之间的高度变化而具有挑战性。通过将现有完善的特征描述符集成到学习算法中，可以自动提取用于图像分类的信息性高级特征。作为一种具有灵活表示和良好全局搜索能力的学习算法，遗传编程可以做到这一点。在本文中，开发了一种新的基于遗传编程的特征学习方法，以自动选择和组合五个现有的完善的描述符来提取图像分类的高级特征。新方法可以从不同类型的图像中自动学习各种数量的全局和/或局部特征。结果表明，新方法在不同难度的八个数据集上的几乎所有比较中都取得了明显更好的分类性能。进一步的分析揭示了新方法在寻找最有效的特征描述符或它们的组合以提取不同分类任务的判别特征的有效性。