计算机科学 ›› 2020, Vol. 47 ›› Issue (6): 176-179.doi: 10.11896/jsjkx.190600142
杨旺功, 淮永建
YANG Wang-gong, HUAI Yong-jian
摘要： 为了提高花朵图像识别与分类的准确率,采用基于深度卷积生成对抗网络的算法来完成花朵图像的识别与分类。为了保证花朵图像在卷积过程中的特征完整性,将不同尺寸的真实花朵图像进行定量平均分块,忽略分块尺寸的大小,保证分块数量相等,然后对分块的图像进行深度卷积池化增强,增强方法为最大值增强,并对噪声进行最大值池化操作,然后将两者进行对抗判别,运用交叉熵误差对价值函数进行评估,求解花朵图像识别与分类的结果。文中分别对花朵图像增强、同类花朵图像识别和不同类花朵图像分类分别进行了实例仿真,实验结果表明,所提算法在花朵图像分类正确率方面的优势明显且稳定性好。
中图分类号:
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