{"title":"基于排序和多视图学习的医学媒体数据区域图像检索","authors":"Wei Huang, Shuru Zeng, Guang Chen","doi":"10.1109/ACII.2015.7344672","DOIUrl":null,"url":null,"abstract":"In this study, a novel region-based image retrieval approach via ranking and multi-view learning techniques is introduced for the first time based on medical multi-modality data. A surrogate ranking evaluation measure is derived, and direct optimization via gradient ascent is carried out based on the surrogate measure to realize ranking and learning. A database composed of 1000 real patients data is constructed and several popular pattern recognition methods are implemented for performance evaluation compared with ours. It is suggested that our new method is superior to others in this medical image retrieval utilization from the statistical point of view.","PeriodicalId":6863,"journal":{"name":"2015 International Conference on Affective Computing and Intelligent Interaction (ACII)","volume":"50 1","pages":"845-850"},"PeriodicalIF":0.0000,"publicationDate":"2015-09-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"3","resultStr":"{\"title\":\"Region-based image retrieval based on medical media data using ranking and multi-view learning\",\"authors\":\"Wei Huang, Shuru Zeng, Guang Chen\",\"doi\":\"10.1109/ACII.2015.7344672\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"In this study, a novel region-based image retrieval approach via ranking and multi-view learning techniques is introduced for the first time based on medical multi-modality data. A surrogate ranking evaluation measure is derived, and direct optimization via gradient ascent is carried out based on the surrogate measure to realize ranking and learning. A database composed of 1000 real patients data is constructed and several popular pattern recognition methods are implemented for performance evaluation compared with ours. It is suggested that our new method is superior to others in this medical image retrieval utilization from the statistical point of view.\",\"PeriodicalId\":6863,\"journal\":{\"name\":\"2015 International Conference on Affective Computing and Intelligent Interaction (ACII)\",\"volume\":\"50 1\",\"pages\":\"845-850\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2015-09-21\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"3\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2015 International Conference on Affective Computing and Intelligent Interaction (ACII)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/ACII.2015.7344672\",\"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 International Conference on Affective Computing and Intelligent Interaction (ACII)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ACII.2015.7344672","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Region-based image retrieval based on medical media data using ranking and multi-view learning
In this study, a novel region-based image retrieval approach via ranking and multi-view learning techniques is introduced for the first time based on medical multi-modality data. A surrogate ranking evaluation measure is derived, and direct optimization via gradient ascent is carried out based on the surrogate measure to realize ranking and learning. A database composed of 1000 real patients data is constructed and several popular pattern recognition methods are implemented for performance evaluation compared with ours. It is suggested that our new method is superior to others in this medical image retrieval utilization from the statistical point of view.
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