近似最近邻搜索中投影增强型残差量化方法

为了降低图像特征向量量化的近似表示和高维向量带来的码书训练时间开销,提出了一种投影增强型残差量化方法。在前期的增强型残差量化工作基础上,将主成分分析与增强型残差量化相结合,使得码书训练和特征量化均在低维向量空间进行以提高效率;在低维向量空间上训练码书过程中,提出了联合优化方法,同时考虑投影和量化产生的总体误差,提升码书精度;针对该量化方法,设计了一种特征向量之间的近似欧氏距离快速计算方法用于近似最近邻完全检索。结果表明,相比增强型残差量化,在相同检索精度前提条件下,投影增强型残差量化的只需花费近1/3的训练时间;相比其它同类方法,所提出方法在码书训练时间效率、检索速度和精度上均具有更优的综合性能。该研究为主成分分析同其它量化模型的有效结合提供了参考。

Projection-based enhanced residual quantization for approximate nearest neighbor search

To reduce the time costs on approximating vector quantization of image features and training codebooks for high-dimensional vectors, a projection-based enhanced residual vector quantization was proposed. Based on previous research on enhance residual quantization (ERVQ), the principle component analysis (PCA) was combined with ERVQ, then both training codebooks and quantizing feature vectors were done in low-dimensional vector space to improve the efficiency. The features for training codebooks were projected into low-dimensional vector spaces. The overall errors generated by projection and quantization were both considered in training procedure to increase the codebook discrimination. For the proposed quantization method, a method to fast computing the approximate Euclidian distance between vectors was designed to retrieve approximate nearest neighbor exhaustively. The experimental results show that the proposed approach only takes near 1/3 training time compared to ERVQ on the condition of same retrieval accuracy. Meanwhile, the proposed approach outperforms the other state-of-the-arts over time efficiency on training codebooks, retrieval accuracy and efficiency. This study provides a reference for the effective combination of PCA with other quantization models.