  Deep learning generally suffers from enormous computational resources and
time-consuming training processes. Broad Learning System (BLS) and its
convolutional variants have been proposed to mitigate these issues and have
achieved superb performance in image classification. However, the existing
convolutional-based broad learning system (C-BLS) either lacks an efficient
training method and incremental learning capability or suffers from poor
performance. To this end, we propose a convolutional broad learning system
(ConvBLS) based on the spherical K-means (SKM) algorithm and two-stage
multi-scale (TSMS) feature fusion, which consists of the convolutional feature
(CF) layer, convolutional enhancement (CE) layer, TSMS feature fusion layer,
and output layer. First, unlike the current C-BLS, the simple yet efficient SKM
algorithm is utilized to learn the weights of CF layers. Compared with random
filters, the SKM algorithm makes the CF layer learn more comprehensive spatial
features. Second, similar to the vanilla BLS, CE layers are established to
expand the feature space. Third, the TSMS feature fusion layer is proposed to
extract more effective multi-scale features through the integration of CF
layers and CE layers. Thanks to the above design and the pseudo-inverse
calculation of the output layer weights, our proposed ConvBLS method is
unprecedentedly efficient and effective. Finally, the corresponding incremental
learning algorithms are presented for rapid remodeling if the model deems to
expand. Experiments and comparisons demonstrate the superiority of our method.
