基于多哈希算法的大规模图像快速检索方法

哈希技术被视为最有潜力的相似性搜索方法,其可以用于大规模多媒体数据搜索场合。为了解决在大规模图像情况下,数据检索效率低下的问题,提出了一种基于分段哈希码的倒排索引树结构,该索引结构将哈希码进行分段处理,对每段哈希码维护一个倒排索引树结构,并结合高效的布隆过滤器构建哈希索引结构。为了进一步提高检索准确性,设计了一种准确的排序融合算法,对多个哈希算法的排序结果分别构建加权无向图,采用PageRank的思想对基于多个哈希算法的排序列表的融合技术进行了详细的说明。实验结果表明,基于分段哈希码的倒排索引树结构能极大地提升数据的检索速度。此外,相比于传统的单个哈希算法排序技术,基于多个哈希算法的排序列表融合技术的检索准确率优势显著。

A fast retrieval method for large scale images based on multiple Hash algorithms

Hash technique is regarded as the most promising method of similarity search, which can be used in large scale multimedia data search. In order to solve the problem of low efficiency of data retrieval over large scale images, we propose a reverse index tree structure based on segmental Hash codes, and elaborate its implementation principles. In this structure, Hash codes are segmented. We design an inverted tree index structure for each section of Hash codes, and build the Hash index structure with the combination of the bloom filters. To further improve the accuracy of the retrieval results, we design a fusing algorithm which constructs the weighted undirected graph separately for the ranking results of multiple Hash algorithms. The fusion algorithm of the sorting list based on multiple Hash algorithms is described in detail by PageRank. Experimental results show that the structure of the inverted index tree based on segmental Hash codes can greatly improve data retrieval speed. Compared with the conventional single Hash algorithm sorting technique, the rank fusion technique for ordered lists of multiple Hash algorithms has obvious superiority in retrieval accuracy.