{"title":"自适应图像检索及其在水下目标识别中的应用","authors":"J. Salazar, M. Azimi-Sadjadi","doi":"10.1109/ACSSC.2004.1399413","DOIUrl":null,"url":null,"abstract":"This paper presents a study on an adaptable image retrieval system used for underwater target identification. Shape and textural features extracted from contrast and range electro-optical imagery data are used to represent each mine-like or non-mine-like sample image. The retrieval system is an adaptable two-layer network where the first layer is structurally adaptable in response to relevance feedback from expert users, while the second layer is adaptable only when a new class is introduced. Each node in the second layer represents one sample image in the training database. Test results on a large electro-optical imagery database are presented, which show the promise of the proposed system as an adaptable image retrieval system.","PeriodicalId":396779,"journal":{"name":"Conference Record of the Thirty-Eighth Asilomar Conference on Signals, Systems and Computers, 2004.","volume":"352 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2004-11-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"1","resultStr":"{\"title\":\"Adaptable image retrieval with application to underwater target identification\",\"authors\":\"J. Salazar, M. Azimi-Sadjadi\",\"doi\":\"10.1109/ACSSC.2004.1399413\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"This paper presents a study on an adaptable image retrieval system used for underwater target identification. Shape and textural features extracted from contrast and range electro-optical imagery data are used to represent each mine-like or non-mine-like sample image. The retrieval system is an adaptable two-layer network where the first layer is structurally adaptable in response to relevance feedback from expert users, while the second layer is adaptable only when a new class is introduced. Each node in the second layer represents one sample image in the training database. Test results on a large electro-optical imagery database are presented, which show the promise of the proposed system as an adaptable image retrieval system.\",\"PeriodicalId\":396779,\"journal\":{\"name\":\"Conference Record of the Thirty-Eighth Asilomar Conference on Signals, Systems and Computers, 2004.\",\"volume\":\"352 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2004-11-07\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"1\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Conference Record of the Thirty-Eighth Asilomar Conference on Signals, Systems and Computers, 2004.\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/ACSSC.2004.1399413\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Conference Record of the Thirty-Eighth Asilomar Conference on Signals, Systems and Computers, 2004.","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ACSSC.2004.1399413","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Adaptable image retrieval with application to underwater target identification
This paper presents a study on an adaptable image retrieval system used for underwater target identification. Shape and textural features extracted from contrast and range electro-optical imagery data are used to represent each mine-like or non-mine-like sample image. The retrieval system is an adaptable two-layer network where the first layer is structurally adaptable in response to relevance feedback from expert users, while the second layer is adaptable only when a new class is introduced. Each node in the second layer represents one sample image in the training database. Test results on a large electro-optical imagery database are presented, which show the promise of the proposed system as an adaptable image retrieval system.