Categorization based Relevance Feedback Search Engine for Earth Observation Images Repositories

Presently Earth observation (EO) satellites acquire huge volumes of high resolution images very much over-passing the capacity of the users to access the information content of the acquired data. Thus, in addition to the existing methods for EO data and information extraction, new methods and tools are needed to explore and help to discover the information hidden in large EO image repositories. This article presents a categorisation based Relevance Feedback (RF) search engine for EO images repositories The developed method is presented as well results obtained for a SPOT5 satellite image database.

[1]  Christos Faloutsos,et al.  MindReader: Querying Databases Through Multiple Examples , 1998, VLDB.

[2]  Seiji Yamada,et al.  Relevance feedback document retrieval using support vector machines , 2004, 2004 IEEE International Joint Conference on Neural Networks (IEEE Cat. No.04CH37541).

[3]  Ingemar J. Cox,et al.  The Bayesian image retrieval system, PicHunter: theory, implementation, and psychophysical experiments , 2000, IEEE Trans. Image Process..

[4]  Michael McGill,et al.  Introduction to Modern Information Retrieval , 1983 .

[5]  J.G. Daugman,et al.  Entropy reduction and decorrelation in visual coding by oriented neural receptive fields , 1989, IEEE Transactions on Biomedical Engineering.

[6]  Yueting Zhuang,et al.  Image retrieval and relevance feedback using peer indexing , 2002, Proceedings. IEEE International Conference on Multimedia and Expo.

[7]  Edward Y. Chang,et al.  Support vector machine active learning for image retrieval , 2001, MULTIMEDIA '01.

[8]  S. Sclaroff,et al.  ImageRover: a content-based image browser for the World Wide Web , 1997, 1997 Proceedings IEEE Workshop on Content-Based Access of Image and Video Libraries.

[9]  Marin Ferecatu,et al.  Reducing the Redundancy in the Selection of Samples for SVM-based Relevance Feedback , 2004 .

[10]  Qi Tian,et al.  Incorporate support vector machines to content-based image retrieval with relevance feedback , 2000, Proceedings 2000 International Conference on Image Processing (Cat. No.00CH37101).

[11]  Nuno Vasconcelos,et al.  A Bayesian framework for content-based indexing and retrieval , 1998, Proceedings DCC '98 Data Compression Conference (Cat. No.98TB100225).

[12]  Matthew L. Miller,et al.  The Bayesian Image Retrieval System, PicHunter , 2000 .