Organizing multimedia big data using semantic based video content extraction technique

With the proliferation of the internet, video has become the principal source. Video big data introduce many hi-tech challenges, which include storage space, broadcast, compression, analysis, and identification. The increase in multimedia resources has brought an urgent need to develop intelligent methods to process and organize them. The combination between multimedia resources and Semantic link Network provides a new prospect for organizing them with their semantics. The tags and surrounding texts of multimedia resources are used to measure their association relation. There are two evaluation methods namely clustering and retrieval are used to measure the semantic relatedness between images accurately and robustly. This method is effective on image searching task. The semantic gap between semantics and video visual appearance is still a challenge. A model for generating the association between video resources using Semantic Link Network model is proposed. The user can select the attributes or concepts as the search query. This is done by providing the knowledge conduction during information extraction and by applying fuzzy reasoning. The first action line is related to the establishment of techniques for the dynamic management of video analysis based on the knowledge gathered in the semantic network. This helps the decisions taken during the analysis process. Based on a set of rules it is able to handle the fuzziness of the annotations provided by the analysis modules gathered in the semantic network.