RBM: Rule Based Lexical Meaning Extraction in Video Data Using Fuzzy

Motivated by the needs of semantic search and retrieval of multimedia contents, operating directly on the video based annotations can be thought as a reasonable way for meeting these needs as video is a common standard providing a wide multimedia content description schema. Raw data and low-level features alone are not sufficient to fulfill the user’s needs; that is, a deeper understanding of the content at the semantic level is required. We propose a semantic content extraction system that allows the user to query and retrieve objects, events, and concepts that are extracted automatically. In automatic extraction process, starts with object and define class for each process in video data. Here we declare a novel ontology based fuzzy video data semantic model uses spatial/temporal relation in event and concept definition. Objects extracted from consecutive representative frames are processed to extract temporal relations. This metaontology definition provides a wide-domain applicable rule construction standard that allows the user to construct ontology for a given domain. In addition to that, we use additional rule to lower spatial relation computation cost and to be able to define some complex situations more effectively. Event extraction process uses objects, spatial relations between objects and temporal relations between events. Similarly, objects and events are used in concept extraction process.