| Technology and Methodology |
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A parameterized evaluation model of similarity on multi-target retrieval for remote sensing image |
| ZENG Zhi1, ZHOU Yongfu2, DU Zhenhong3, LIU Renyi3 |
1. Department of Computer Science, Huizhou University, Huizhou 516007, China;
2. Electronic and Information Engineering Institute, Heyuan Polytechnic, Heyuan 517000, China;
3. Institute of GIS, Zhejiang University, Hangzhou 310028, China |
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Abstract In content-based image retrieval (CBIR), the feature selection of target retrieval and similarity computing is a hot issue of current research. As the object-oriented image processing methods are conducive to describing the spatial relationships between objects, the authors proposed an parameterized evaluation model of similarity for multi-target retrieval on the basis of an analysis of multi-feature descriptor. This model not only takes into account the global spatial relationship between image objects but also considers the local features of image targets. The model organizes the multi-dimensional features for an image using the analytic hierarchy process (AHP)method under moving picture expert group (MPEG7)standard, then the eigenvalues are calculated using existing methods, and finally the weighting coefficients are set for calculating the overall similarity on the basis of user preferences so as to evaluate the effect of multi-target retrieval. Thus it can provide a powerful reference and experience for multi-target retrieval in an image database. The experimental results show that target retrieval is not only related to the method of similarity calculation but also related to the complexity of the image itself.
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| Keywords
snow depth
MODIS snow cover
elevation
multivariate nonlinear regression Kriging(MNRK)
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Issue Date: 23 July 2015
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2015,
Vol. 27
