Iterative quantization hashing algorithm does not consider that most of high-dimensional visual descriptors for images exhibit a natural matrix structure. When high-dimensional feature vectors represented the visual descriptors and assigned long binary codes, a random projection matrix requires expensive complexities in both space and time. To address the above issues, this paper proposed a hashing image retrieval method based on bilinear iterative quantization, which mapped the high-dimensional into two smaller projection matrices using compact bilinear projections instead of a single large projection matrix. Then it minimized the quantization error in an iterative quantization way and generate effective hash code. Experiments on two datasets, CIFAR-10 and Caltech256. This method achieved comparable retrieval accuracy to the state-of-the-art 8 hashing methods while having orders of magnitude faster linear scanning time and smaller memory footprint. The results show that the proposed method can reduce the impact of the high dimensionality of data and improve the performance of ITQ, the algorithm can widely serve the hashing image retrieval application of high-dimensional data and long coded bits.

[2020-08-05] Printed Article /doc/2019.02.0066/v2