The distributed system for inverted multi-index visual retrieval

With the explosive growth of visual databases, it is infeasible to maintain the huge indexing structures within the memory of a single server. In this paper, a distributed visual retrieval system based on inverted multi-index is proposed to generate a huge codebook with very low memory consumption and time cost. In order to improve the performance of product quantization, a vector space decomposition strategy is performed by affinity propagation clustering. In the meantime, a distributed framework is introduced to inverted multi-index to improve the time efficiency. Our works are validated on the large scale database of INRIA Holidays and Flickr 1M. The results of our experiments indicate that the performance of PQ is greatly improved and the visual retrieval system is speeded up at comparable precision.