基于优化分块颜色直方图及模糊C聚类的彩色图像检索方法

将数据挖掘的聚类算法应用到基于内容的图像检索中可以有效提高检索的速度和效果。模糊聚类算法更符合图像检索本身所具有的模糊性,但这种方法存在聚类分析时间过久影响检索性能的问题,因此本文提出了一种基于优化分块颜色直方图及模糊C聚类的彩色图像检索方法。首先对图像库中的每幅图像进行分块,并提取出每一块的优化颜色特征信息;然后采用模糊C均值聚类算法对得到的颜色特征向量进行聚类,得到每个图像类的聚类中心;最后计算查询示例图像和对应图像类的图像之间的相似度,按照相似度的大小返回检索结果。实验表明,本文提出的方法不仅具有较高的查全率和查准率,而且提取的特征维数较少,聚类时间短,检索速度快。

A Color Image Retrieval Method Based on Improved Blocked Color Histograms and Fuzzy C-means Clustering

The application of data mining clustering algorithms in content-based image retrieval can effectively optimize the retrieval speed and effect,to be more specific,fuzzy clustering algorithms fit better the fuzzy characteristics of image retrieval,but affect the retrieval function with a long clustering time,so a color image retrieval method based on improved blocked color histograms and fuzzy c-means clustering is proposed.First,each image in the image library is blocked,the improved color characteristic information of each block is extracted;a fuzzy c-means clustering algorithm is used to cluster color feature vectors,and each cluster center of image class is obtained;finally,the similarity between the sample image and the corresponding categories is calculated,returning the retrieval results according to the size of similarity.The experiments show that the proposed method has a higher recall rate and a higher precision rate,and less feature dimension of extraction,a shorter clustering time and a quicker retrieval speed.