CSF: Crowdsourcing semantic fusion for heterogeneous media big data in the internet of things

Abstract

With the rising popularity of social media in the context of environments based on the Internet of things (IoT), semantic information has emerged as an important bridge to connect human intelligence with heterogeneous media big data. As a critical tool to improve media big data retrieval, semantic fusion encounters a number of challenges: the manual method is inefficient, and the automatic approach is inaccurate. To address these challenges, this paper proposes a solution called CSF (Crowdsourcing Semantic Fusion) that makes full use of the collective wisdom of social users and introduces crowdsourcing computing to semantic fusion. First, the correlation of cross-modal semantics is mined and the semantic objects are normalized for fusion. Second, we employ the dimension reduction and relevance feedback approaches to reduce non-principal components and noise. Finally, we research the storage and distribution mechanism. Experiment results highlight the efficiency and accuracy of the proposed approach. The proposed method is an effective and practical cross-modal semantic fusion and distribution mechanism for heterogeneous social media, provides a novel idea for social media semantic processing, and uses an interactive visualization framework for social media knowledge mining and retrieval to improve semantic knowledge and the effect of representation.