完整後設資料紀錄
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dc.contributor.authorLin, Fang-Juen_US
dc.contributor.authorWang, Tsai-Peien_US
dc.date.accessioned2018-08-21T05:53:44Z-
dc.date.available2018-08-21T05:53:44Z-
dc.date.issued2018-06-01en_US
dc.identifier.issn1380-7501en_US
dc.identifier.urihttp://dx.doi.org/10.1007/s11042-017-4948-7en_US
dc.identifier.urihttp://hdl.handle.net/11536/145092-
dc.description.abstractImage classification is a core task in many applications of computer vision. Recognition of weather conditions based on large-volume image datasets is a challenging problem. However, there has been little research on weather-related recognition using color images, particularly with large datasets. In this study, we proposed a metric learning framework to investigate a two-class weather classification problem. We improve the classification accuracy using metric learning approaches. Extracting features from images is a challenging task and practical requirements such as domain knowledge and human intervention. In this paper, we define several categories of weather feature cures based on observations of outdoor images captured under different weather conditions. Experimental results show that a classifier based on metric learning framework is effective in weather classification and outperforms the previous approach when using the same dataset.en_US
dc.language.isoen_USen_US
dc.subjectMetric learningen_US
dc.subjectk nearest neighboren_US
dc.subjectInformation-theoretic metric learningen_US
dc.subjectLarge-margin nearest-neighbor metric learningen_US
dc.subjectWeather featuresen_US
dc.subjectWeather image classificationen_US
dc.titleMetric learning for weather image classificationen_US
dc.typeArticleen_US
dc.identifier.doi10.1007/s11042-017-4948-7en_US
dc.identifier.journalMULTIMEDIA TOOLS AND APPLICATIONSen_US
dc.citation.volume77en_US
dc.citation.spage13309en_US
dc.citation.epage13321en_US
dc.contributor.department資訊工程學系zh_TW
dc.contributor.departmentDepartment of Computer Scienceen_US
dc.identifier.wosnumberWOS:000434382900011en_US
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