Main Reference PaperAutomatic Semantic Content Extraction in videos using a fuzzy ontology and rule Based Model, 2013.
  • Metaontology definition provides a wide-domain applicable rule construction standard that allows the user to construct ontology for a given domain .This project presents an ontology-based fuzzy video semantic content model, additional rule definitions (without using ontology). Ontology model uses spatial and temporal relations in event and concept definitions.

+ Description
  • Metaontology definition provides a wide-domain applicable rule construction standard that allows the user to construct ontology for a given domain .This project presents an ontology-based fuzzy video semantic content model, additional rule definitions (without using ontology). Ontology model uses spatial and temporal relations in event and concept definitions.

  • To develop intelligent methods to model and extract the video content.

  • To assist in video interpretation without domain knowledge.

  • To retrieve some desired content from massive amounts of video data in an efficient and semantically meaningful manner.

  • To extract the semantic content automatically from video.

+ Aim & Objectives
  • To develop intelligent methods to model and extract the video content.

  • To assist in video interpretation without domain knowledge.

  • To retrieve some desired content from massive amounts of video data in an efficient and semantically meaningful manner.

  • To extract the semantic content automatically from video.

  • The proposed system proposes the three significant functions First, the concept of a video event graph, to learn the event structure from training videos has been presented. Second one is, correlation graph, the event correlation graph signifies the frequency of occurrence of conditionally dependent sub-event. Normalized cut is an unbiased method of partitioning a graph V into two (or more) segments.

+ Contribution
  • The proposed system proposes the three significant functions First, the concept of a video event graph, to learn the event structure from training videos has been presented. Second one is, correlation graph, the event correlation graph signifies the frequency of occurrence of conditionally dependent sub-event. Normalized cut is an unbiased method of partitioning a graph V into two (or more) segments.

  • Java JDK 1.8, MySQL 5.5.40

  • Netbeans 8.0.1 and J2SE

+ Software Tools & Technologies
  • Java JDK 1.8, MySQL 5.5.40

  • Netbeans 8.0.1 and J2SE

  • B.E / B.Tech / M.E / M.Tech

+ Project Recommended For
  • B.E / B.Tech / M.E / M.Tech

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