Combined association, segregation and aggregation analysis by Zhao L. P.
By Zhao L. P.
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Additional resources for Combined association, segregation and aggregation analysis on case control family data
Due to the relatively uniform distribution of the RGB bands, we chose 8 uniform quantization bins for each of the RGB bands. For the HSV color space histogram, we took the statistical nature of the HSV space into consideration, along with human sensitivity to hue and saturation, and uniformly quantized the hue, saturation and value histograms into 12,5,5 bins respectively [lo]. The similarity metric that we implemented for the actual retrieval was the histogram intersection . Fig. 8. bat image and the color sea green Reprinted from  with permission from Ekevier.
Prior t o the similarity matching process, the best cluster to perform the retrieval is determined from the query feature descriptor and the cluster centroid using the nearest-neighbor algorithm. 75% retrieval precision in average for offloading the computation with an order 0 ( 1 / N ) where N=10. The inter-dependence between each individual image database can be used to improve the retrieval precision for a centralized CBR system, using the same algorithm proposed for a distributed CBR system.
Thus, we need to rank the retrieval results based on the magnitude of D and the angle, LD, that it makes with the equidistant line. To do this we combine the two values using a weighted sum: where lower rank values 72. imply images with a closer match to both the query colors. , magnitude or angle, are to dominate. 2 give the most robust results. This is to be expected since collinearity with the equidistant line does not necessarily imply a match with any query color. It implies that each query color is equally close to the indexed colors.