{"title":"基于显著性图和距离度量学习的昆虫识别图像检索","authors":"Susumu Genma, Takahiro Ogawa, M. Haseyama","doi":"10.1109/GCCE.2016.7800330","DOIUrl":null,"url":null,"abstract":"This paper presents an image retrieval method for insect identification based on saliency map and distance metric learning. First, the proposed method extracts regions of insects from target images by using saliency map and calculates visual features from the extracted insect regions. Next, in order to realize accurate retrieval of insects based on the calculated features, distance metric learning is newly adopted. Consequently, through users' evaluation in the retrieval, optimal distance can be obtained for the calculated visual features to obtain successful retrieval results, and the identification of insects becomes feasible. Experimental results show the effectiveness of our method.","PeriodicalId":416104,"journal":{"name":"2016 IEEE 5th Global Conference on Consumer Electronics","volume":"34 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2016-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"4","resultStr":"{\"title\":\"Image retrieval for identification of insects based on saliency map and distance metric learning\",\"authors\":\"Susumu Genma, Takahiro Ogawa, M. Haseyama\",\"doi\":\"10.1109/GCCE.2016.7800330\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"This paper presents an image retrieval method for insect identification based on saliency map and distance metric learning. First, the proposed method extracts regions of insects from target images by using saliency map and calculates visual features from the extracted insect regions. Next, in order to realize accurate retrieval of insects based on the calculated features, distance metric learning is newly adopted. Consequently, through users' evaluation in the retrieval, optimal distance can be obtained for the calculated visual features to obtain successful retrieval results, and the identification of insects becomes feasible. Experimental results show the effectiveness of our method.\",\"PeriodicalId\":416104,\"journal\":{\"name\":\"2016 IEEE 5th Global Conference on Consumer Electronics\",\"volume\":\"34 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2016-10-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"4\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2016 IEEE 5th Global Conference on Consumer Electronics\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/GCCE.2016.7800330\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2016 IEEE 5th Global Conference on Consumer Electronics","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/GCCE.2016.7800330","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Image retrieval for identification of insects based on saliency map and distance metric learning
This paper presents an image retrieval method for insect identification based on saliency map and distance metric learning. First, the proposed method extracts regions of insects from target images by using saliency map and calculates visual features from the extracted insect regions. Next, in order to realize accurate retrieval of insects based on the calculated features, distance metric learning is newly adopted. Consequently, through users' evaluation in the retrieval, optimal distance can be obtained for the calculated visual features to obtain successful retrieval results, and the identification of insects becomes feasible. Experimental results show the effectiveness of our method.