Semantic-preload video model based on VOP coding

In recent years, in order to reduce semantic gap which exists between high-level semantics and low-level features of video when the human understanding image or video, people mostly try the method of video annotation where in signal’s downstream, namely further (again) attach labels to the content in video-database. Few people focus on the idea that: Use limited interaction and the means of comprehensive segmentation (including optical technologies) from the front-end of collection of video information (i.e. video camera), with video semantics analysis technology and corresponding concepts sets (i.e. ontology) which belong in a certain domain, as well as story shooting script and the task description of scene shooting etc; Apply different-level semantic descriptions to enrich the attributes of video object and the attributes of image region, then forms a new video model which is based on Video Object Plan (VOP) Coding. This model has potential intellectualized features, and carries a large amount of metadata, and embedded intermediate-level semantic concept into every object. This paper focuses on the latter, and presents a framework of a new video model. At present, this new video model is temporarily named “Video Model of Semantic-Preloaded or Semantic-Preload Video Model (simplified into VMoSP or SPVM)”. This model mainly researches how to add labeling to video objects and image regions in real time, here video object and image region are usually used intermediate semantic labeling, and this work is placed on signal’s upstream (i.e. video capture production stage). Because of the research needs, this paper also tries to analyses the hierarchic structure of video, and divides the hierarchic structure into nine hierarchy semantic levels, of course, this nine hierarchy only involved in video production process. In addition, the paper also point out that here semantic level tagging work (i.e. semantic preloading) only refers to the four middle-level semantic. All in all, this research was unfolded is based on analyzed the characteristic of the existing video mode, and with reference to MPEG series standard.

[1]  Ahmet M. Kondoz,et al.  Multi-rate variable-quality VOP shape coder , 1999, Proceedings IEEE International Conference on Multimedia Computing and Systems.

[2]  Alexander G. Hauptmann,et al.  Towards a Large Scale Concept Ontology for Broadcast Video , 2004, CIVR.

[3]  Ingrid Daubechies,et al.  The wavelet transform, time-frequency localization and signal analysis , 1990, IEEE Trans. Inf. Theory.

[4]  Uwe Rauschenbach,et al.  STOCHASTIC MOTION COHERENCY ANALYSIS FOR MOTION VECTOR FIE LD SEGMENTATION ON COMPRESSED VIDEO SEQUENCES , 2005 .

[5]  Jing Xiao,et al.  Content-Based Video Indexing and Retrieval , 2004 .

[6]  Liang-Gee Chen,et al.  Efficient moving object segmentation algorithm using background registration technique , 2002, IEEE Trans. Circuits Syst. Video Technol..

[7]  Yo-Sung Ho,et al.  A VOP generation tool: automatic segmentation of moving objects in image sequences based on spatio-temporal information , 1999, IEEE Trans. Circuits Syst. Video Technol..

[8]  Meng Wang,et al.  Unified Video Annotation via Multigraph Learning , 2009, IEEE Transactions on Circuits and Systems for Video Technology.

[9]  Kôiti Hasida,et al.  Semantics of multimedia in MPEG-7 , 2002, Proceedings. International Conference on Image Processing.

[10]  S. Natarajan An efficient Video Segmentation Algorithm with Real time Adaptive Threshold Technique , 2009 .

[11]  Jingsong He,et al.  A Novel Edge Detection Technique with Orientation-Based Similarity and Immunological Principles , 2007, Third International Conference on Natural Computation (ICNC 2007).

[12]  A. Murat Tekalp,et al.  Integrated semantic-syntactic video modeling for search and browsing , 2004, IEEE Transactions on Multimedia.

[13]  A. Murat Tekalp,et al.  Automatic extraction of low-level object motion descriptors , 2001, Proceedings 2001 International Conference on Image Processing (Cat. No.01CH37205).

[14]  Adrian Hilton,et al.  Graph-based foreground extraction in extended color space , 2009, 2009 16th IEEE International Conference on Image Processing (ICIP).

[15]  Ping Wang,et al.  Inter-frame Correlation Based Compressed Video Steganalysis , 2008, 2008 Congress on Image and Signal Processing.

[16]  Fred Stentiford,et al.  An Attention Based Focus Control System , 2006, 2006 International Conference on Image Processing.