Full text: Papers accepted on the basis of peer-reviewed abstracts (Part B)

In: Wagner W., Székely, B. (eds.): ISPRS TC VII Symposium - 100 Years ISPRS, Vienna, Austria, July 5-7, 2010, IAPRS, Voi. XXXVIII, Part 7B 
5. CONCLUSION 
The amount of imagery data increases rapidly, mainly due to 
the launching of new generation of high resolution satellites 
(WorldView-1, TerraSAR-X, WorldView-2). 
Multiple terabytes of HRSIDB are being collected by many 
nations across the globe. This raises the question how to 
retrieve, manage and make best use of the HRSIDB 
information. 
Content-based analysis of all high resolution imagery is a 
seriously limited by time constraints, and a solution for the 
content-based image retrieval problem is urgently needed. 
Also, a new framework is lacking to support content-based 
search and different levels of analysis and generalization. 
Our research proposes a model for homogeneous pattern sketch. 
The model allows to discern visually meaningful content of a 
textural pattern. It helps to overcome distinctions between the 
classes of GUMs in terms of their visual representation. 
The experiments show the model parameters’ flexibility and the 
capacity of training and self-training. 
(b) 
Figure 2.0utlining results of homogeneous region of Ikonos- 
image (lm): (a) - according to model (1), (b) - manual. 
Most importantly, this model can be used for automated 
generation of interpretation results and metadata, and it offers 
sufficient computational efficiency to support the formalization 
of ecological expertise and global-environmental-databases. 
(b) 
Figure 1. Outlining homogeneous region of Quickbird-image 
(0,7m): (a) - according to model (1), (b) - manual.. 
References: 
Braveen, M., 2009. Evaluation of Content Based Image 
Retrieval Systems Based on Color Feature. International 
Journal of Recent Trends in Engineering (IJRTE), 1(2), pp.57- 
62. 
Brodatz, P., 1966. Texture: a Photographic Album for Artists 
and Designers. NY: Dover. 
Gimel'farb, G., 1999. Image Textures and Gibbs Random 
Fields. Kluwer Academic Publishers, 250 p. 
Kelly, P.M., 1995. Query by image example: the CANDID 
approach. In: Storage and Retrieval for Image and Video 
Databases III, SPIE-2420, Los Alamos National Laboratory 
Technical Report LA-UR-95-374 T.M., pp 238-248. 
Kovalevskaya, N., 2002. From Laboratory Spectroscopy to 
Remotely Sensed Spectra of Terrestrial Ecosystems. Kluwer 
Academic Publishers, pp. 121 -147. 
Ma, W., 1996. Texture Features and Learning Similarity. Proc. 
IEEE International Conference on Computer Vision and 
Pattern Recognition. San Francisco, 1996. pp. 425-430. 
Manjunath, B.S., 1996. Texture features for browsing and 
retrieval of image data. IEEE Transactions on Pattern Analysis 
and Machine Intelligence, 18(8), pp. 837-842. 
Marr, D., 1982. Vision: A Computational Investigation into the 
Human Representation and Processing of Visual Information. 
W.H. Freeman and Company, NY. 
Smith, D., 1996. A Digital library for geographically referenced 
materials. IEEE Computer, pp. 54-60. 
Veltkamp, R.C., 2001. Features in Content-based Image 
Retrieval Systems: a Survey, State-of-the-Art in Content-Based
	        
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