Issue No.01 - Jan. (2013 vol.25)
Y. Yildirim , Dept. of Comput. Eng., Middle East Tech. Univ., Ankara, Turkey
A. Yazici , Dept. of Comput. Eng., Middle East Tech. Univ., Ankara, Turkey
T. Yilmaz , Dept. of Comput. Eng., Middle East Tech. Univ., Ankara, Turkey
DOI Bookmark: http://doi.ieeecomputersociety.org/10.1109/TKDE.2011.189
Recent increase in the use of video-based applications has revealed the need for extracting the content in videos. 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. Currently, manual techniques, which are inefficient, subjective and costly in time and limit the querying capabilities, are being used to bridge the gap between low-level representative features and high-level semantic content. Here, we propose a semantic content extraction system that allows the user to query and retrieve objects, events, and concepts that are extracted automatically. We introduce an ontology-based fuzzy video semantic content model that uses spatial/temporal relations in event and concept definitions. This metaontology definition provides a wide-domain applicable rule construction standard that allows the user to construct an ontology for a given domain. In addition to domain ontologies, we use additional rule definitions (without using ontology) to lower spatial relation computation cost and to be able to define some complex situations more effectively. The proposed framework has been fully implemented and tested on three different domains. We have obtained satisfactory precision and recall rates for object, event and concept extraction.
video retrieval, feature extraction, fuzzy set theory, knowledge based systems, ontologies (artificial intelligence), domain ontology, automatic semantic content extraction system, ontology-based fuzzy video semantic content model, rule-based model, video-based applications, video content extraction, querying capability, low-level representative features, high-level semantic content, object retrieval, event retrieval, concept retrieval, spatial-temporal relations, metaontology definition, wide-domain applicable rule construction standard, Semantics, Videos, Ontologies, Feature extraction, Data mining, Data models, Visualization, ontology, Semantic content extraction, video content modeling, fuzziness
Y. Yildirim, A. Yazici, T. Yilmaz, "Automatic Semantic Content Extraction in Videos Using a Fuzzy Ontology and Rule-Based Model", IEEE Transactions on Knowledge & Data Engineering, vol.25, no. 1, pp. 47-61, Jan. 2013, doi:10.1109/TKDE.2011.189