Jisuanji kexue (Aug 2021)
Image Retrieval Method Based on Fuzzy Color Features and Fuzzy Smiliarity
Abstract
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 outperformsthat 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 thanthat 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.
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