{"title":"遥感图像处理的协作跨域$k$NN搜索","authors":"Ying Zhong, Wei Weng, Jianmin Li, Shunzhi Zhu","doi":"10.1109/LGRS.2019.2906686","DOIUrl":null,"url":null,"abstract":"<inline-formula> <tex-math notation=\"LaTeX\">$k$ </tex-math></inline-formula>NN search is a fundamental function in image processing, which is useful in many real applications, including image cluster, image classification, and image understanding and analysis in general. In this light, we propose and study a novel collaborative cross-domain <inline-formula> <tex-math notation=\"LaTeX\">$k$ </tex-math></inline-formula>NN search (CD-<inline-formula> <tex-math notation=\"LaTeX\">$k$ </tex-math></inline-formula>NN) in multidomain space. Given a query location <inline-formula> <tex-math notation=\"LaTeX\">$q$ </tex-math></inline-formula> in a multidomain space (e.g., spatial domain, temporal domain, textual domain, and so on), the CD-<inline-formula> <tex-math notation=\"LaTeX\">$k$ </tex-math></inline-formula>NN finds top-<inline-formula> <tex-math notation=\"LaTeX\">$k$ </tex-math></inline-formula> data points with the minimum distance to <inline-formula> <tex-math notation=\"LaTeX\">$q$ </tex-math></inline-formula>. This problem is challenging due to two reasons. First, how to define practical distance measures to evaluate the distance in multidomain space. Second, how to prune the search space efficiently in multiple domains. To address the challenges, we define a linear combination method-based distance measure for multidomain space. Based on the distance measure, a collaborative search method is developed to constrain the CD search space in a comparable smaller range. A pair of upper and lower bounds is defined to prune the search space in multiple domains effectively. Finally, we conduct extensive experiments to verify that the developed methods can achieve a high performance.","PeriodicalId":13046,"journal":{"name":"IEEE Geoscience and Remote Sensing Letters","volume":"16 1","pages":"1801-1805"},"PeriodicalIF":4.0000,"publicationDate":"2019-04-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1109/LGRS.2019.2906686","citationCount":"3","resultStr":"{\"title\":\"Collaborative Cross-Domain $k$ NN Search for Remote Sensing Image Processing\",\"authors\":\"Ying Zhong, Wei Weng, Jianmin Li, Shunzhi Zhu\",\"doi\":\"10.1109/LGRS.2019.2906686\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<inline-formula> <tex-math notation=\\\"LaTeX\\\">$k$ </tex-math></inline-formula>NN search is a fundamental function in image processing, which is useful in many real applications, including image cluster, image classification, and image understanding and analysis in general. In this light, we propose and study a novel collaborative cross-domain <inline-formula> <tex-math notation=\\\"LaTeX\\\">$k$ </tex-math></inline-formula>NN search (CD-<inline-formula> <tex-math notation=\\\"LaTeX\\\">$k$ </tex-math></inline-formula>NN) in multidomain space. Given a query location <inline-formula> <tex-math notation=\\\"LaTeX\\\">$q$ </tex-math></inline-formula> in a multidomain space (e.g., spatial domain, temporal domain, textual domain, and so on), the CD-<inline-formula> <tex-math notation=\\\"LaTeX\\\">$k$ </tex-math></inline-formula>NN finds top-<inline-formula> <tex-math notation=\\\"LaTeX\\\">$k$ </tex-math></inline-formula> data points with the minimum distance to <inline-formula> <tex-math notation=\\\"LaTeX\\\">$q$ </tex-math></inline-formula>. This problem is challenging due to two reasons. First, how to define practical distance measures to evaluate the distance in multidomain space. Second, how to prune the search space efficiently in multiple domains. To address the challenges, we define a linear combination method-based distance measure for multidomain space. Based on the distance measure, a collaborative search method is developed to constrain the CD search space in a comparable smaller range. A pair of upper and lower bounds is defined to prune the search space in multiple domains effectively. Finally, we conduct extensive experiments to verify that the developed methods can achieve a high performance.\",\"PeriodicalId\":13046,\"journal\":{\"name\":\"IEEE Geoscience and Remote Sensing Letters\",\"volume\":\"16 1\",\"pages\":\"1801-1805\"},\"PeriodicalIF\":4.0000,\"publicationDate\":\"2019-04-18\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"https://sci-hub-pdf.com/10.1109/LGRS.2019.2906686\",\"citationCount\":\"3\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"IEEE Geoscience and Remote Sensing Letters\",\"FirstCategoryId\":\"5\",\"ListUrlMain\":\"https://doi.org/10.1109/LGRS.2019.2906686\",\"RegionNum\":3,\"RegionCategory\":\"地球科学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q2\",\"JCRName\":\"ENGINEERING, ELECTRICAL & ELECTRONIC\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"IEEE Geoscience and Remote Sensing Letters","FirstCategoryId":"5","ListUrlMain":"https://doi.org/10.1109/LGRS.2019.2906686","RegionNum":3,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"ENGINEERING, ELECTRICAL & ELECTRONIC","Score":null,"Total":0}
Collaborative Cross-Domain $k$ NN Search for Remote Sensing Image Processing
$k$ NN search is a fundamental function in image processing, which is useful in many real applications, including image cluster, image classification, and image understanding and analysis in general. In this light, we propose and study a novel collaborative cross-domain $k$ NN search (CD-$k$ NN) in multidomain space. Given a query location $q$ in a multidomain space (e.g., spatial domain, temporal domain, textual domain, and so on), the CD-$k$ NN finds top-$k$ data points with the minimum distance to $q$ . This problem is challenging due to two reasons. First, how to define practical distance measures to evaluate the distance in multidomain space. Second, how to prune the search space efficiently in multiple domains. To address the challenges, we define a linear combination method-based distance measure for multidomain space. Based on the distance measure, a collaborative search method is developed to constrain the CD search space in a comparable smaller range. A pair of upper and lower bounds is defined to prune the search space in multiple domains effectively. Finally, we conduct extensive experiments to verify that the developed methods can achieve a high performance.
期刊介绍:
IEEE Geoscience and Remote Sensing Letters (GRSL) is a monthly publication for short papers (maximum length 5 pages) addressing new ideas and formative concepts in remote sensing as well as important new and timely results and concepts. Papers should relate to the theory, concepts and techniques of science and engineering as applied to sensing the earth, oceans, atmosphere, and space, and the processing, interpretation, and dissemination of this information. The technical content of papers must be both new and significant. Experimental data must be complete and include sufficient description of experimental apparatus, methods, and relevant experimental conditions. GRSL encourages the incorporation of "extended objects" or "multimedia" such as animations to enhance the shorter papers.