| Abstrakt: |
When the fine-grained recognition problems of image classification processed, broad learning system (BLS) is more efficient in classification, but has difficulty in distinguishing features with large similarities. Sparse representation classification (SRC) is more capable of handling similarity features, but is more computationally expensive. To better use the BLS model to tackle the fine-grained recognition problem and improve the ability to handle similarity features, this article combines the advantages of BLS and SRC, and proposes a broad sparse fine-grained image classification model based on dictionary selection strategy, dictionary broad sparse representation classification (DBSRC). First, to solve the parameter selection problem of the BLS model, leave one out cross validation (LOO) is introduced to quickly find the better regularization parameters and build a BLS-LOO coarse-grained classification model. Then propose reliability criteria based on a threshold selection strategy for the selection of fine-grained images. Next, an adaptive dictionary selection strategy is designed based on the output of the BLS-LOO to construct a sparse subdictionary for each fine-grained image that is not distinguished by the BLS-LOO. Finally, a sparse subdictionary based SRC model is used to classify fine-grained images. Experimental results show that DBSRC achieves good classification performance on three image datasets with different complex dimensions, ImageNet, USPS, and Pavia, and has strong processing capability for fine-grained features. |
