基于模糊颜色特征和模糊相似度的图像检索方法

Image Retrieval Method Based on Fuzzy Color Features and Fuzzy Smiliarity

基于内容的图像检索系统的性能主要依赖于两个关键技术:图像特征提取和图像特征匹配。文中提取了所有图像的颜色特征,并在颜色特征提取过程中采用了适当的模糊算法以得到图像的模糊颜色特征。图像特征匹配主要取决于两个图像特征向量之间的相似度,文中提出了一种新的模糊相似度衡量方法,该方法利用给定的查询图像与其k幅近邻图像之间的相似度构成查询图像的k维模糊特征向量,利用每幅被检索图像与查询图像的k幅近邻图像之间的相似度构成每幅被检索图像的k维模糊特征向量,计算查询图像的k维模糊特征向量与每幅被检索图像的k维模糊特征向量之间的模糊相似度,并将检索到的图像按照模糊相似度按从大到小的顺序反馈给用户。为了验证提出的模糊颜色特征的有效性,文中在WANG数据集上进行了一系列的实验对比;为了衡量基于不同相似度的图像检索系统的性能,文中在WANG,Corel-5k和Corel-10k数据集上分别进行了一系列的实验对比。实验结果表明,基于最大最小值的图像检索系统的性能优于基于其他3种常用相似度的图像检索系统的性能,而基于模糊相似度的图像检索系统的性能优于基于最大最小值的图像检索系统的性能。在WANG,Corel-5k和Corel-10k数据集上,基于模糊相似度的图像检索系统检索到的前20幅图像的平均查准率比基于最大最小值的图像检索系统检索到的前20幅图像的平均查准率分别高4.92%,17.11%和19.48%;基于模糊相似度的图像检索系统检索到的前100幅图像的平均查准率比基于最大最小值的图像检索系统检索到的前100幅图像的平均查准率分别高4.94%,22.61%和33.02%。

The performance of content-based image retrieval(CBIR)system mainly depends on two key technologies:image feature extraction and image feature matching.In this paper,the color features of all the images are extracted,and an appropriate fuzzy algorithm is adopted in the process of color feature extraction to gain the fuzzy color features of image.Image feature ma-tching mainly depends on the similarity between two image feature vectors.In this paper,a novel fuzzy similarity measure method is proposed it adopts the similarity between the query image and its k nearest neighbor images to constitute the k-dimensional fuzzy feature vector of the query imagem,and adopts the similarity between each retrieved image and k nearest neighbor images of the query image to constitute the k-dimensional fuzzy feature vector of each retrieved image.Then it calculates the fuzzy similarity between the k-dimensional fuzzy feature vector of the query image and the k-dimensional fuzzy feature vector of each retrieved image,and the retrieved images are fed back to users in reverse order of the fuzzy similarity.In order to verify the effectiveness of the proposed fuzzy color features,a series of experimental comparison are performed on the WANG dataset.In order to evaluate the performance of the image retrieval system based on different similarities,a series of experimental comparison are performed on WANG,Corel-5k and Corel-10K datasets.Experimental results show that the performance of the image retrieval system based on the maximum and minimum value outperforms that of the image retrieval systems based on the other three commonly used similarities.And the performance of the image retrieval system based on fuzzy similarity outperforms that of the image retrieval system based on the maximum and minimum value.On the WANG,Corel-5k and Corel-10K datasets,the average precision of top 20 images retrieved by the image retrieval system based on fuzzy similarity is 4.92%,17.11%and 19.48%higher than that of top 20 images retrieved by the image retrieval system based on the maximum and minimum value respectively,and the average precision of top 100 images retrieved by the image retrieval system based on fuzzy similarity is 4.94%,22.61%and 33.02%higher that than of top 100 images retrieved by the image retrieval system based on the maximum and minimum value respectively.