Mining method for target feature data in color image database
YANG Pin-lin
Abstract：Aiming at the problem that the uncertainty caused by the multiple features of color image data is easy to appear in the object feature mining, a new mining method for the target feature data in the color image database was proposed. The color image data were clustered with the subtractive clustering method, and the clustered data were classified with the outlier detection technique. In addition, the optimal target image feature data were selected with the quantum behaved particle swarm optimization method. In combination with the structural similarity calculation method, the mining of optimal target image feature data was realized. The results show that compared with the traditional mining method, the recall rate of proposed method reduces by about 17%, while the mining accuracy increases by about 28. 6%.
Keywords：color imagedatabasetarget characteristicdata miningdata clusteringsimilarity calculationoutlier detectionparticle swarm optimization
Publication Date：2018-01-01
Online Publishing Date：2025-08-15(First online date of this platform, not the publication date of the document)
Pages：5( 60-64 )