一种基于DSmT推理的物品融合识别算法

针对目前提升深度模型分类表现方法存在的硬件性能不足、结构创新不易、训练样本有限等问题,提出一种基于DSmT(Dezert-Smarandache)推理的物品融合识别算法。对于待识别目标,应用数据融合思想将来自不同深度学习模型提供的识别信息进行融合处理。利用已有的预训练深度学习模型,根据分类识别任务进行特定的微调;针对DSmT理论中构造信度赋值困难的问题,使用深度学习网络对图像的判别输出进行证据源信度赋值;在决策级层运用DSmT组合理论对信度赋值融合处理,进而实现物品的准确识别。在不改变网络模型结构与同一数据集的情况下,将提出的方法与单一网络模型和平均值处理方法进行对比测试试验。试验结果表明,该方法可以有效地提高物品图像的识别率。

An object fusion recognition algorithm based on DSmT

Aimed at improving the performance of the depth model in image classification currently, i.e. the inadequate performance of existing hardware, difficulty in structural innovation and the limited training samples, an object fusion recognition algorithm based on DSmT(Desert-Smarandache theory)was proposed. The recognition information of objects was collected and fused from different learning network models. The pretrained depth learning models were fine-tuned according to the classification task. To solve the problem in the construction of the basic belief assignment(BBA)in DSmT, the models were used to assign the BBA to the evidence sources. The DSmT combination theory was used in the fusion of the decision-layer in order to raise the recognition rate. Under the conditions of unchanged network models and the dataset, the multi-model fusion method with the single-model and average value method were compared in the experiments. The results of the experiments showed that the algorithm could improve correct recognition ratio effectively under the same conditions.