Atoany N. Fierro-Radilla, K. Toscano-Medina, M. Nakano-Miyatake, H. Meana, M. Cedillo-Hernández, F. Garcia-Ugalde
{"title":"基于内容的图像检索系统的自动视觉特征权重获取","authors":"Atoany N. Fierro-Radilla, K. Toscano-Medina, M. Nakano-Miyatake, H. Meana, M. Cedillo-Hernández, F. Garcia-Ugalde","doi":"10.1109/ICEEE.2015.7357918","DOIUrl":null,"url":null,"abstract":"In Content-Based Image Retrieval (CBIR) Systems it is necessary to combine more than one visual descriptor in order to improve the retrieval performance. The most common descriptors are Color-Based, Shape-Based and Texture-Based descriptors. When more than one visual descriptor is linearly combined, some adequate weight must be assigned to each visual feature. The most common manner is setting the same weight value for each visual feature. The sum of these values must be equal to one. However, this process does not guarantee the optimum performance of the CBIR system. In order to guarantee the best performance, it is necessary to do several experimentations to find the optimum weight values combination. This is time consuming process and ambiguous, due to the weights values depends on the nature of the databases. In this paper we proposed a scheme which computes automatically the best weight combination and guarantees the optimum performance of the CBIR system.","PeriodicalId":285783,"journal":{"name":"2015 12th International Conference on Electrical Engineering, Computing Science and Automatic Control (CCE)","volume":"48 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2015-12-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"1","resultStr":"{\"title\":\"Automatic visual features weights obtention for Content-Based Image Retrieval Systems\",\"authors\":\"Atoany N. Fierro-Radilla, K. Toscano-Medina, M. Nakano-Miyatake, H. Meana, M. Cedillo-Hernández, F. Garcia-Ugalde\",\"doi\":\"10.1109/ICEEE.2015.7357918\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"In Content-Based Image Retrieval (CBIR) Systems it is necessary to combine more than one visual descriptor in order to improve the retrieval performance. The most common descriptors are Color-Based, Shape-Based and Texture-Based descriptors. When more than one visual descriptor is linearly combined, some adequate weight must be assigned to each visual feature. The most common manner is setting the same weight value for each visual feature. The sum of these values must be equal to one. However, this process does not guarantee the optimum performance of the CBIR system. In order to guarantee the best performance, it is necessary to do several experimentations to find the optimum weight values combination. This is time consuming process and ambiguous, due to the weights values depends on the nature of the databases. In this paper we proposed a scheme which computes automatically the best weight combination and guarantees the optimum performance of the CBIR system.\",\"PeriodicalId\":285783,\"journal\":{\"name\":\"2015 12th International Conference on Electrical Engineering, Computing Science and Automatic Control (CCE)\",\"volume\":\"48 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2015-12-17\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"1\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2015 12th International Conference on Electrical Engineering, Computing Science and Automatic Control (CCE)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/ICEEE.2015.7357918\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2015 12th International Conference on Electrical Engineering, Computing Science and Automatic Control (CCE)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ICEEE.2015.7357918","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Automatic visual features weights obtention for Content-Based Image Retrieval Systems
In Content-Based Image Retrieval (CBIR) Systems it is necessary to combine more than one visual descriptor in order to improve the retrieval performance. The most common descriptors are Color-Based, Shape-Based and Texture-Based descriptors. When more than one visual descriptor is linearly combined, some adequate weight must be assigned to each visual feature. The most common manner is setting the same weight value for each visual feature. The sum of these values must be equal to one. However, this process does not guarantee the optimum performance of the CBIR system. In order to guarantee the best performance, it is necessary to do several experimentations to find the optimum weight values combination. This is time consuming process and ambiguous, due to the weights values depends on the nature of the databases. In this paper we proposed a scheme which computes automatically the best weight combination and guarantees the optimum performance of the CBIR system.