Toward High-Level Visual Information Retrieval

Content-based visual information retrieval (CBVIR) as a new generation (with new concepts, techniques, mechanisms, etc.) of visual information retrieval has attracted many interests from the database community. The research starts by using a low-level feature from more than a dozen years ago. The current focus has shifted to capture high-level semantics of visual information. This chapter will convey the research from the feature level to the semantic level by treating the problem of semantic gap under the general framework of CBVIR. This high-level research is the so-called semantic-based visual information retrieval (SBVIR). This chapter first shows some statistics about the research publications on semantic-based retrieval in recent years; it then presents some existing approaches based on multi-level image retrieval and multi-level video retrieval. It also gives an overview of several current centers of attention by summarizing certain results on subjects such as image and video

[1]  Yixin Chen,et al.  CLUE: cluster-based retrieval of images by unsupervised learning , 2005, IEEE Transactions on Image Processing.

[2]  Milind R. Naphade,et al.  Extracting semantics from audio-visual content: the final frontier in multimedia retrieval , 2002, IEEE Trans. Neural Networks.

[3]  Yu-Jin Zhang,et al.  Mining for Image Classification Based on Feature Elements , 2005 .

[4]  Adil Alpkocak,et al.  Combining textual and visual clusters for semantic image retrieval and auto-annotation , 2005 .

[5]  Konstantinos N. Plataniotis,et al.  Query feedback for interactive image retrieval , 2004, IEEE Transactions on Circuits and Systems for Video Technology.

[6]  Yu-Jin Zhang,et al.  Camera attention weighted strategy for video shot grouping , 2005, Visual Communications and Image Processing.

[7]  Sushmita Mitra,et al.  Web mining: a survey in the fuzzy framework , 2004, Fuzzy Sets Syst..

[8]  Yu-Jin Zhang,et al.  AdaBoost in region-based image retrieval , 2004, 2004 IEEE International Conference on Acoustics, Speech, and Signal Processing.

[9]  Yin Xu,et al.  Association Feedback: A Novel Tool for Feature Elements Based Image Retrieval , 2001, IEEE Pacific Rim Conference on Multimedia.

[10]  Jiebo Luo,et al.  Pictures are not taken in a vacuum - an overview of exploiting context for semantic scene content understanding , 2006, IEEE Signal Processing Magazine.

[11]  Ling Guan,et al.  Semantic Retrieval of Multimedia by Concept Languages , 2006 .

[12]  David S. Doermann,et al.  The Indexing and Retrieval of Document Images: A Survey , 1998, Comput. Vis. Image Underst..

[13]  J. T. Robinson,et al.  Progressive search and retrieval in large image archives , 1998, IBM J. Res. Dev..

[14]  Feng Xu,et al.  Feature Selection for Image Categorization , 2006, ACCV.

[15]  Terumasa Aoki,et al.  MPEG-7 based dozen dimensional digital content architecture for semantic image retrieval services , 2004, IEEE International Conference on e-Technology, e-Commerce and e-Service, 2004. EEE '04. 2004.

[16]  Sumeet Singh,et al.  Refining image retrieval based on context-driven methods , 1998, Electronic Imaging.

[17]  Feng Xu,et al.  Atmosphere-based image classification through luminance and hue , 2005, Visual Communications and Image Processing.

[18]  Yu-Jin Zhang.  Advanced Techniques for Object-Based Image Retrieval , 2005, Encyclopedia of Information Science and Technology.

[19]  Toshikazu Kato,et al.  Database architecture for content-based image retrieval , 1992, Electronic Imaging.

[20]  Haibin Lu,et al.  A hierarchical organization scheme for video data , 2002, Pattern Recognit..

[21]  Shih-Fu Chang,et al.  A fully automated content-based video search engine supporting spatiotemporal queries , 1998, IEEE Trans. Circuits Syst. Video Technol..

[22]  Tianli Yu,et al.  Retrieval of video clips using global motion information , 2001 .

[23]  Daniel A. Keim,et al.  An Efficient Approach to Clustering in Large Multimedia Databases with Noise , 1998, KDD.

[24]  Shih-Fu Chang,et al.  Model-based classification of visual information for content-based retrieval , 1998, Electronic Imaging.

[25]  Jesse S. Jin,et al.  Combining intra-image and inter-class semantics for consumer image retrieval , 2005, Pattern Recognit..

[26]  Sougata Mukherjea,et al.  Integration of Image Matching and Classification for Multimedia Navigation , 2004, Multimedia Tools and Applications.

[27]  Alan Hanjalic,et al.  Video and image retrieval beyond the cognitive level: the needs and possibilities , 2001, IS&T/SPIE Electronic Imaging.

[28]  James Ze Wang,et al.  SIMPLIcity: Semantics-Sensitive Integrated Matching for Picture LIbraries , 2001, IEEE Trans. Pattern Anal. Mach. Intell..

[29]  Shih-Fu Chang,et al.  Image Retrieval: Current Techniques, Promising Directions, and Open Issues , 1999, J. Vis. Commun. Image Represent..

[30]  K. Nagao,et al.  Weblog-style video annotation and syndication , 2005, First International Conference on Automated Production of Cross Media Content for Multi-Channel Distribution (AXMEDIS'05).

[31]  Dimitrios Androutsos,et al.  Small World Distributed Access of Multimedia Data , 2006 .

[32]  Yin Xu,et al.  Semantic Retrieval Based on Feature Element Constructional Model and Bias Competition Mechanism , 2003, IS&T/SPIE Electronic Imaging.

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

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

[35]  Yu-Jin Zhang,et al.  News Video Indexing and Abstraction by Specific Visual Cues: MSC and News Caption , 2005 .

[36]  Michael Lindenbaum,et al.  A Generic Grouping Algorithm and Its Quantitative Analysis , 1998, IEEE Trans. Pattern Anal. Mach. Intell..

[37]  Yu-Jin Zhang,et al.  Object-Based Techniques for Image Retrieval , 2004 .

[38]  Hirohide Haga,et al.  A usability survey of a contents-based video retrieval system by combining digital video and an electronic bulletin board , 2005 .

[39]  Yu-Jin Zhang,et al.  Unbalanced region matching based on two-level description for image retrieval , 2005, Pattern Recognit. Lett..

[40]  Borko Furht,et al.  Content-based visual information retrieval , 2002 .

[41]  Ramesh C. Jain,et al.  A survey on the use of pattern recognition methods for abstraction, indexing and retrieval of images and video , 2002, Pattern Recognit..

[42]  Mehdi Khosrow-Pour,et al.  Printed at: , 2011 .

[43]  Nikolay S. Merzlyakov,et al.  Object recognition and matching for image retrieval , 2002, Other Conferences.

[44]  Yu Fu,et al.  Self-adaptive relevance feedback based on multilevel image content analysis , 2001, IS&T/SPIE Electronic Imaging.

[45]  Tianli Yu,et al.  Motion feature extraction for content-based video sequence retrieval , 2000, IS&T/SPIE Electronic Imaging.

[46]  Djemel Ziou,et al.  Relevance feedback for CBIR: a new approach based on probabilistic feature weighting with positive and negative examples , 2006, IEEE Transactions on Image Processing.

[47]  Antonio Criminisi,et al.  Object categorization by learned universal visual dictionary , 2005, Tenth IEEE International Conference on Computer Vision (ICCV'05) Volume 1.

[48]  Tzu-Chuan Chou,et al.  A semantic learning for content-based image retrieval using analytical hierarchy process , 2005, Expert Syst. Appl..

[49]  Yin Xu,et al.  Feature element theory for image recognition and retrieval , 2001, IS&T/SPIE Electronic Imaging.

[50]  Yu-Jin Zhang.  New Advancements in Image Segmentation for CBIR , 2005, Encyclopedia of Information Science and Technology.

[51]  Bernt Schiele,et al.  Performance evaluation and optimization for content-based image retrieval , 2006, Pattern Recognit..