摘要 : An efficient novel strategy for color-based image retrieval is introduced. It is a hybrid approach combining a data compression scheme based on self-organizing neural networks with a nonparametric statistical test for comparing ve... 展开 An efficient novel strategy for color-based image retrieval is introduced. It is a hybrid approach combining a data compression scheme based on self-organizing neural networks with a nonparametric statistical test for comparing vectorial distributions. First, the color content in each image is summarized by representative RGB-vectors extracted using the Neural-Gas network. The similarity between two images is then assessed as commonality between the corresponding representative color distributions and quantified using the multivariate Wald-Wolfowaz test. Experimental results drawn from the application to a diverse collection of color images show a significantly improved performance (approximately 10-15% higher) relative to both the popular, simplistic approach of color histogram and the sophisticated, computationally demanding technique of Earth Mover's Distance. (c) 2006 Elsevier Inc. All rights reserved. 收起