A multi-feature broad learning system (MFBLS) is proposed to improve the image classification performance of broad learning system (BLS) and its variants. The model is characterized by two major characteristics: multi-feature extraction method and parallel structure. Multi-feature extraction method is utilized to improve the feature-learning ability of BLS. The method extracts four features of the input image, namely convolutional feature, K-means feature, HOG feature and color feature. Besides, a parallel architecture that is suitable for multi-feature extraction is proposed for MFBLS. There are four feature blocks and one fusion block in this structure. The extracted features are used directly as the feature nodes in the feature block. In addition, a “stacking with ridge regression” strategy is applied to the fusion block to get the final output of MFBLS. Experimental results show that MFBLS achieves the accuracies of 92.25%, 81.03%, and 54.66% on SVHN, CIFAR-10, and CIFAR-100, respectively, which outperforms BLS and its variants. Besides, it is even superior to the deep network, convolutional deep belief network, in both accuracy and training time on CIFAR-10. Code for the paper is available at https://github.com/threedteam/mfbls.

中文翻译：

---

用于图像分类的多特征广泛学习系统

为了提高广义学习系统（BLS）及其变体的图像分类性能，提出了一种多特征广义学习系统（MFBLS）。该模型具有两大特点：多特征提取方法和并行结构。利用多特征提取方法来提高BLS的特征学习能力。该方法提取输入图像的四个特征，即卷积特征、K-means特征、HOG特征和颜色特征。此外，针对MFBLS提出了一种适用于多特征提取的并行架构。该结构中有四个特征块和一个融合块。提取的特征直接用作特征块中的特征节点。此外，将“带岭回归的堆叠”策略应用于融合块以获得 MFBLS 的最终输出。实验结果表明，MFBLS 在 SVHN、CIFAR-10 和 CIFAR-100 上的准确率分别达到了 92.25%、81.03% 和 54.66%，优于 BLS 及其变体。此外，它在 CIFAR-10 上的准确性和训练时间上甚至优于深度网络、卷积深度信念网络。该论文的代码可在https://github.com/threedteam/mfbls.