NDLI: Multiscale Markov random fields for large image datasets representation

Content Provider	IEEE Xplore Digital Library
Author	Rehrauer, H. Seidel, K. Datcu, M.
Copyright Year	1997
Description	Author affiliation: Swiss Fed. Inst. of Technol., Zurich, Switzerland (Rehrauer, H.)
Abstract	Future users of satellite images will be faced with a huge amount of data. The development of "content-based image retrieval algorithms for remote sensing image archives" will allow them to efficiently use the upcoming databases of large images. The authors present an image segmentation and feature extraction algorithm, that will enable users to search images by content. In their approach, images are modelled by multiscale Markov random fields (MSRF). This model is superior to spatial Markov random field models in that it is able to describe the long range as well as the short range behaviour of the image data. Image information extracted at multiple scales is incorporated naturally in the model. Additionally it is computationally less costly than the spatial random field models. The difference to similar work by Ch.A. Bouman et al. (1994) and A. Jain et al. (1992) is that the multiscale process is not only used to find a reasonable segmentation of the image, but that the estimated parameters of the scale process serve also as image features. These image features together with the textural characteristics of the image are stored hierarchically in a pyramidal structure from large to small scales. Thereby even large datasets can be quickly browsed.
Starting Page	255
Ending Page	257
File Size	472396
Page Count	3
File Format	PDF
ISBN	0780338367
DOI	10.1109/IGARSS.1997.615855
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	1997-08-03
Publisher Place	Singapore
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Markov random fields Image segmentation Satellites Information retrieval Image retrieval Remote sensing Image databases Spatial databases Feature extraction Data mining
Content Type	Text
Resource Type	Article

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