关键词: 图像检索, 训练样本集, 多维特征向量, 遗传算法, 最优加权系数

Abstract: In allusion to that the single feature and manual setting multi-dimensional weighting features cannot satisfy the need of CBIR more and more, we presents a new algorithm of multi-dimensional features vector weighting based on the cluster of training sample. The algorithm needs to manually build the training sample, extract the multi-dimensional features like color, texture and shape of each element, and then use the genetic algorithm to find the most optimal weighting coefficient set of features vectors sample. Finally, it calculates the proper value of the training sample using the set and retrieve the sample. Proved by the experiment, this algorithm can improve the classified accuracy compared to other algorithm and has a high accuracy in differentiating two clusters with high similarity.

Key words: image retrieval, training sample cluster, multi-dimensional features vector, GA, most optimal weighting coefficient set