基于最小重构误差的优化局部聚合描述符向量图像检索算法

Optimized vector of locally aggregated descriptor algorithm in image retrieval based on minimized reconstruction error

针对局部聚合描述符向量(VLAD)模型中对特征软量化时权重系数的取值不确定性和特征量化误差较大问题,提出一种具有最小重构误差的权重系数分配算法.该算法以最小化重构误差为标准,将具有最小化重构误差的稀疏编码的编码系数作为软量化VLAD的权重系数.数据库的图像检索测试结果表明,该算法相比主流的VLAD特征编码算法所得图像检索精度可提高10%左右,且有更小的特征重构误差.

Aiming at the uncertainty value of weight coefficient and the big error of characteristic quantification in soft assignment of characteristics quantification in Vector of Locally Aggregated Descriptor（ VLAD） model, an efficient weight coefficient soft quantization assignment algorithm based on minimized reconstruction error was proposed. The sparse coding coefficients with the minimized reconstruction errors were taken as the weighting values of soft quantization assignment based on VLAD by taking the minimized reconstruction error as the standard. The image retrieval test results of database show that,compared with the mainstream VLAD feature coding algorithms, the image retrieval accuracy of the proposed algorithm can be improved about 10%, and the proposed algorithm can obtain a smaller feature reconstruction error.