TMR: Towards an efficient semantic-based heterogeneous transportation media big data retrieval

Abstract

In media retrieval system for intelligent transportation, media data variety and heterogeneity have been one of the most critical features. Documents with different formats may express similar semantic information, thus, searching documents reflecting users׳ intention has been a crucial and important task. For solving this problem, this paper proposes a novel semantic-based heterogeneous transportation media retrieval (TMR) approach to improve the performance. TMR supports the function of retrieving various media types such as image, video, audio and text by using a single media type. Firstly, semantic fields are extracted from the user annotating and automatic learning to express the users׳ intention. Secondly, ontology is used to represent the semantic fields of a media, and the ontology represented semantic information is saved together with the media document data. Thirdly, the semantic field adjustment process is described. Finally, fuzzy matching is employed to measure the similarity between the users׳ intention and media documents. For the returned results, we carry out the performance evaluation models in comparison with the existing approaches. Experimental result indicates the superiority of TMR in term of precision rate, computing speed, storage cost and user experience.