关键词: 近邻可逆性, 多媒体文档, 跨媒体检索

Abstract: There is a phenomenon of neighborhood non-reversibility among single-media data objects. That is, object A is one of K neighbors of object B, but object B is not the neighbor of object A. In fact, similar phenomenon also exists among heterogeneous data objects. Although some good works had been made on neighborhood reversibility of single-media data, no effect has been paid on the neighborhood reversibility problem of heterogeneous data. This paper mainly focuses on the neighborhood reversibility problem of multimedia documents. Firstly, a cross-media retrieval framework, named LE-KNN (Laplacian eigenmaps-K nearest neighbors) is built, which is used to evaluate the effect of neighborhood reversibility in cross-media retrieval. Secondly, based on the framework, two methods are introduced to cross-media search. On the one hand, the neighborhood relationships of multimedia documents are redefined to improve the neighborhood reversibility of multimedia documents. On the other hand, contextual dissimilarity measure (CDM) algorithm is introduced to adjust the distances among single-media data objects in multimedia documents so as to improve the neighborhood reversibility of multimedia documents in dataset. The extensive experiments show that introducing the neighborhood reversibility verifying can remarkably improve the search accuracy of cross-media retrieval.

Key words: neighborhood reversibility, multimedia documents, cross-media retrieval