计算机科学 ›› 2022, Vol. 49 ›› Issue (11A): 210900021-8.doi: 10.11896/jsjkx.210900021
张建新1,2, 吴悦2, 张强2,3, 魏小鹏2,3
ZHANG Jian-xin1,2, WU Yue2, ZHANG Qiang2,3, WEI Xiao-peng2,3
摘要: 近年来深度哈希方法因其存储效率高和查询速度快的优势在大规模图像检索领域受到了广泛关注。为改善深度成对有监督哈希方法在图像检索上的性能,从提高深度哈希获取图像特征的全局性和同类样本相似性角度出发,提出了一种有监督相似性保持的深度二阶哈希方法。该方法采用成对样本图像进行特征建模,并利用协方差估计来捕获样本图像的深度二阶信息,以获取具有良好全局表达能力的深度二阶哈希码;在此基础上,借鉴类哈希近似二值化来解决哈希映射过程中的非凸性问题,以更好地避免量化误差,同时基于多损失函数集成思想构建类别监督和相似性保持的联合约束,进而采用交替迭代的优化方式实现网络的端到端训练,最终确定样本图像的最优哈希码。在3个通用数据集上进行了广泛的实验,结果有效表明了所提出有监督相似性保持的深度二阶哈希方法的有效性。
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