{"title":"彩色图像检索的性能评价","authors":"E. Mendi, Coskun Bayrak","doi":"10.1109/AIPR.2010.5759680","DOIUrl":null,"url":null,"abstract":"In this paper, we have investigated the capabilities of 4 approaches for image search for a CBIR system. First two approaches are based on comparing the images using color histograms of RGB and HSV spaces, respectively. The other 2 approaches are based on two quantitative image fidelity measurements, Mean Square Error (MSE) and Structural Similarity Index (SSIM), which provide a degree of similarity between two images. The precision performances of approaches have been evaluated by using a public image database containing 1000 images. Finally effectiveness of retrieval has been measured for each method.","PeriodicalId":128378,"journal":{"name":"2010 IEEE 39th Applied Imagery Pattern Recognition Workshop (AIPR)","volume":"19 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2010-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"1","resultStr":"{\"title\":\"Performance evaluation of color image retrieval\",\"authors\":\"E. Mendi, Coskun Bayrak\",\"doi\":\"10.1109/AIPR.2010.5759680\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"In this paper, we have investigated the capabilities of 4 approaches for image search for a CBIR system. First two approaches are based on comparing the images using color histograms of RGB and HSV spaces, respectively. The other 2 approaches are based on two quantitative image fidelity measurements, Mean Square Error (MSE) and Structural Similarity Index (SSIM), which provide a degree of similarity between two images. The precision performances of approaches have been evaluated by using a public image database containing 1000 images. Finally effectiveness of retrieval has been measured for each method.\",\"PeriodicalId\":128378,\"journal\":{\"name\":\"2010 IEEE 39th Applied Imagery Pattern Recognition Workshop (AIPR)\",\"volume\":\"19 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2010-10-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"1\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2010 IEEE 39th Applied Imagery Pattern Recognition Workshop (AIPR)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/AIPR.2010.5759680\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2010 IEEE 39th Applied Imagery Pattern Recognition Workshop (AIPR)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/AIPR.2010.5759680","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
In this paper, we have investigated the capabilities of 4 approaches for image search for a CBIR system. First two approaches are based on comparing the images using color histograms of RGB and HSV spaces, respectively. The other 2 approaches are based on two quantitative image fidelity measurements, Mean Square Error (MSE) and Structural Similarity Index (SSIM), which provide a degree of similarity between two images. The precision performances of approaches have been evaluated by using a public image database containing 1000 images. Finally effectiveness of retrieval has been measured for each method.
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