基于CNN和语义信息的军事图像场景识别

Cheng Chen, Jian Huang, Chongyu Pan, Xingsheng Yuan
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引用次数: 7

摘要

针对军事场景的复杂性导致军事场景识别准确率较低的问题,本文提出了一种基于CNN和语义信息的军事图像场景识别方法。首先通过CNN对场景图像进行初步分类,然后利用军事目标与场景之间的语义关系对分类结果进行优化。利用收集到的军事图像场景识别数据集对该方法进行了验证。实验结果表明,该方法比未修改的CNN算法具有更好的准确率,在测试集上取得了良好的识别效果,具有未来应用的潜力。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Military Image Scene Recognition Based on CNN and Semantic Information
Due to the complexity of the military scene and resulted low accuracy in military scene recognition, this paper proposes a military image scene recognition method based on CNN and semantic information. First, the scene image is initially classified by CNN, and then the classification result is optimized by using the semantic relationship between the military target and the scene. This paper uses the collected military image scene recognition dataset to evaluate the proposed method. The experimental results show that the proposed method has better accuracy than the unmodified CNN algorithm and achieve good recognition results on the test set, which has potential for future application.
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