针对目前哈希图像检索技术中多比特位量化方法通过将实数向量的每一维分别量化,割裂了实数向量各个维度之间联系的问题,提出了一种子空间自适应多位编码量化的哈希图像检索方法.该方法对一组实数做量化并且拓展到乘积空间,将实数向量划分为若干个数据子向量.由于子空间的方差和信息量大小呈正相关,该方法可根据子空间的方差计算并分配编码位数,使方差大的子空间拥有更长的编码位数,并且减小了因给不同的子空间分配相同的比特位而引起的信息损失,提高了编码的精度.在公开的图像数据集LabelMe和Flickr上的测试结果表明:与效果最优的同类方法相比,该方法使得量化误差下降了30％,检索结果的平均准确率提升了9.8％,说明可以通过减小量化误差来提升检索精度.%Multi-bit quantization is a popular quantization approach in hashing method to retrieve images,but it separately quantizes each dimension of real values,thus may destroy the original neighborhood structure.In this paper,an adaptive multi-bit quantization method is proposed.The method decomposes the original data space into several subspaces and then extends them to a product space.Since there exists a positive correlation between the variance of each subspace and the amount of information in the subspace,the proposed method adaptively allocates the numbers of bits according to the variance of the subspaces and gives more bits to the subspace with larger variance.The proposed adaptive multi-bit quantization scheme makes the hashing method effectively decrease the distortion compared to those which allocating same bits to different subspaces,and greatly increases coding efficiency.Experiments on two large public image datasets,LabelMe and Flickr,and comparisons with some state-of-the-art hashing methods show that the proposed method reduces the quantization error by 30％,and improves the average accuracy of the retrieval results by up to 9.8％,indicating that the proposed method can largely improve the retrieval efficiency by reducing the quantization error.
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