Study of Various Video Annotation Techniques

Image annotation is an active field of research that serves as a precursor to video annotation. With the increase in the number of videos and important information present in them, there is need to annotate the videos. Annotation improves the efficiency of searching and retrieving the video. Video features are often inspired and sometimes directly borrowed from image techniques and many methods for image indexing are also easily applied to video. A human operator can specify annotations such as time, location and activity. More sophisticated annotations can be provided using automatic or semi-automatic techniques. There are various techniques that can be used for annotation; this paper discusses some of the techniques. Technique which employs the ontology, requires ontology language, web ontology language (OWL) is the ontology language that has gained importance. This paper also discusses ontology and knowledge base editor- Protege.

[1]  Yafei Zhang,et al.  A Framework for Video Event Detection Using Weighted SVM Classifiers , 2009, 2009 International Conference on Artificial Intelligence and Computational Intelligence.

[2]  Thomas R. Gruber,et al.  A translation approach to portable ontology specifications , 1993, Knowl. Acquis..

[3]  Euripides G. M. Petrakis,et al.  SIA: Semantic Image Annotation Using Ontologies and Image Content Analysis , 2010, ICIAR.

[4]  Adam Pease,et al.  Towards a standard upper ontology , 2001, FOIS.

[5]  John R. Smith,et al.  Large-scale concept ontology for multimedia , 2006, IEEE MultiMedia.

[6]  Wei-Ying Ma,et al.  Graph based multi-modality learning , 2005, ACM Multimedia.

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

[8]  Janko Calic,et al.  A rule-based video annotation system , 2004, IEEE Transactions on Circuits and Systems for Video Technology.

[9]  Ramakant Nevatia,et al.  VERL: An Ontology Framework for Representing and Annotating Video Events , 2005, IEEE Multim..

[10]  Marcel Worring,et al.  Content-Based Image Retrieval at the End of the Early Years , 2000, IEEE Trans. Pattern Anal. Mach. Intell..

[11]  Jean-Marc Odobez,et al.  Text detection, recognition in images and video frames , 2004, Pattern Recognit..

[12]  Alberto Del Bimbo,et al.  Video Annotation and Retrieval Using Ontologies and Rule Learning , 2010, IEEE MultiMedia.

[13]  João Ascenso,et al.  Automatic Text Extraction in Digital Video Based on Motion Analysis , 2004, ICIAR.

[14]  Luc Van Gool,et al.  The 2005 PASCAL Visual Object Classes Challenge , 2005, MLCW.

[15]  B. S. Manjunath,et al.  Video Annotation Through Search and Graph Reinforcement Mining , 2010, IEEE Transactions on Multimedia.

[16]  Jianping Fan,et al.  Semi-automatic Video Content Annotation , 2002, IEEE Pacific Rim Conference on Multimedia.

[17]  Salvatore Tabbone,et al.  Classification and Automatic Annotation Extension of Images Using Bayesian Network , 2008, SSPR/SPR.

[18]  Nishchol Mishra,et al.  Developing University Ontology using protégé OWL Tool: Process and Reasoning , 2011 .

[19]  Leszek Cieplinski.  MPEG-7 Color Descriptors and Their Applications , 2001, CAIP.

[20]  Wolfgang Effelsberg,et al.  Automatic text segmentation and text recognition for video indexing , 2000, Multimedia Systems.

[21]  Allan Hanbury,et al.  A survey of methods for image annotation , 2008, J. Vis. Lang. Comput..

[22]  Michael J. Witbrock,et al.  An Introduction to the Syntax and Content of Cyc , 2006, AAAI Spring Symposium: Formalizing and Compiling Background Knowledge and Its Applications to Knowledge Representation and Question Answering.

[23]  Yung-Yu Chuang,et al.  Multi-cue fusion for semantic video indexing , 2008, ACM Multimedia.

[24]  Alberto Del Bimbo,et al.  Visual information retrieval , 1999 .

[25]  Christiane Fellbaum,et al.  Book Reviews: WordNet: An Electronic Lexical Database , 1999, CL.

[26]  Amir-Masoud Eftekhari-Moghadam,et al.  Fuzzy rule-based reasoning approach for event detection and annotation of broadcast soccer video , 2013, Appl. Soft Comput..

[27]  Paul Clough,et al.  The IAPR TC-12 Benchmark: A New Evaluation Resource for Visual Information Systems , 2006 .

[28]  Antonio Torralba,et al.  LabelMe: A Database and Web-Based Tool for Image Annotation , 2008, International Journal of Computer Vision.

[29]  Chong-Wah Ngo,et al.  Domain adaptive semantic diffusion for large scale context-based video annotation , 2009, 2009 IEEE 12th International Conference on Computer Vision.

[30]  Jin-Woo Jeong,et al.  Ontology-based automatic video annotation technique in smart TV environment , 2011, IEEE Transactions on Consumer Electronics.

[31]  Deborah L. McGuinness,et al.  Owl web ontology language guide , 2003 .

[32]  Christophe Marsala,et al.  Automatic video annotation with forests of fuzzy decision trees , 2008, SOCO 2008.

[33]  Andrew Salway,et al.  Temporal Information in Collateral Texts for Indexing Movies , 2002 .

[34]  Riichiro MIZOGUCHI,et al.  Tutorial on ontological engineering Part 2: Ontology development, tools and languages , 2004, New Generation Computing.

[35]  Kanad K. Biswas,et al.  Generic Video Classification: An Evolutionary Learning Based Fuzzy Theoretic Approach , 2002, ICVGIP.

[36]  Dunja Mladenic,et al.  Spatio-temporal reasoning for traffic scene understanding , 2011, 2011 IEEE 7th International Conference on Intelligent Computer Communication and Processing.

[37]  David S. Doermann,et al.  Automatic text detection and tracking in digital video , 2000, IEEE Trans. Image Process..

[38]  Dimitrios Makris,et al.  A Framework for Ontology Enriched Semantic Annotation of CCTV Video , 2007, Eighth International Workshop on Image Analysis for Multimedia Interactive Services (WIAMIS '07).

[39]  Jing Hua,et al.  Region-based Image Annotation using Asymmetrical Support Vector Machine-based Multiple-Instance Learning , 2006, 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'06).