An effective and efficient parallel large-scale cross-media retrieval in mobile cloud network

With the rapid growth of multimedia data (e.g., text, image, video, audio and 3D model, etc) in the web, there are a large number of media objects with different modalities in the multimedia documents such as webpages, which exhibit latent semantic correlation. As a new type of multimedia retrieval method, cross-media retrieval is becoming increasingly attractive, through which users can get the results with various media types with the same semantic information by submitting a retrieval of any media type. The explosive increasing of the number of media objects, however, makes it difficult for the traditional local standalone mode to process efficiently. So the powerful parallel processing capability of cloud computing is accommodated to facilitate the efficient large-scale cross-media retrieval. In this paper, based on a Multi-Layer-Cross-Reference-Graph(MLCRG) model, we propose an efficient parallel cross-media retrieval (PCMR) method in which two enabling techniques (i.e., 1) the adaptive cross-media data allocation algorithm and 2) the PCIndex scheme) are accommodated to effectively speedup the retrieval performance. To the best of our knowledge, there is little research on the parallel retrieval processing of the large-scale cross-media databases in the mobile cloud network. Extensive experiments are conducted to testify that our proposed PCIndex method outperform the three competitors (e.g., the PFAR (Mao et al, 22), the MBSR (Retrieval 4(2):153-164, 42) and the SPECH (Knowl Based Syst 251(5):1-13, 40)) in terms of the effectiveness and efficiency, respectively.