Locally linear spatial pyramid hash for large-scale image search

Wanqing Zhao, Hangzai Luo, Jinye Peng, Jianping Fan

Abstract: Hash-based methods can achieve a fast similarity search by representing high-dimensional data with compact binary codes. However, the spatial structure in row images was always lost in most previous methods. In this paper, a novel Locally Linear Spatial Pyramid Hash(LLSPH) algorithm is developed for the task of fast image retrieval. Unlike the conventional approach, the spatial extent of image features is exploited in our method. The spatial pyramid structure is used both to construct binary hash codes and to increase the discriminability of the description. To generate interpretable binary codes, the proposed LLSPH method captures the spatial characteristics of the original SPM and generates a low-dimensional sparse representation using multi-dictionaries Locality-constrained Linear Coding(MD_LLC). LLSPH then converts the low-dimensional data into Hamming space by the TF-IDF binarization rule. Our experimental results show that our LLSPH method can outperform several state-of-the-art hashing algorithms on the Caltech256 and ImageNet-500 datasets.