{"title":"目标定位与识别的广义特征学习与索引","authors":"Ning Zhou, A. Angelova, Jianping Fan","doi":"10.1109/WACV.2014.6836100","DOIUrl":null,"url":null,"abstract":"This paper addresses a general feature indexing and retrieval scenario in which a set of features detected in the image can retrieve a relevant class of objects, or classes of objects. The main idea behind those features for general object retrieval is that they are capable of identifying and localizing some small regions or parts of the potential object. We propose a set of criteria which take advantage of the learned features to find regions in the image which likely belong to an object. We further use the features' localization capability to localize the full object of interest and its extents. The proposed approach improves the recognition performance and is very efficient. Moreover, it has the potential to be used in automatic image understanding or annotation since it can uncover regions where the objects can be found in an image.","PeriodicalId":73325,"journal":{"name":"IEEE Winter Conference on Applications of Computer Vision. IEEE Winter Conference on Applications of Computer Vision","volume":"51 1","pages":"198-204"},"PeriodicalIF":0.0000,"publicationDate":"2014-03-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Generalized feature learning and indexing for object localization and recognition\",\"authors\":\"Ning Zhou, A. Angelova, Jianping Fan\",\"doi\":\"10.1109/WACV.2014.6836100\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"This paper addresses a general feature indexing and retrieval scenario in which a set of features detected in the image can retrieve a relevant class of objects, or classes of objects. The main idea behind those features for general object retrieval is that they are capable of identifying and localizing some small regions or parts of the potential object. We propose a set of criteria which take advantage of the learned features to find regions in the image which likely belong to an object. We further use the features' localization capability to localize the full object of interest and its extents. The proposed approach improves the recognition performance and is very efficient. Moreover, it has the potential to be used in automatic image understanding or annotation since it can uncover regions where the objects can be found in an image.\",\"PeriodicalId\":73325,\"journal\":{\"name\":\"IEEE Winter Conference on Applications of Computer Vision. IEEE Winter Conference on Applications of Computer Vision\",\"volume\":\"51 1\",\"pages\":\"198-204\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2014-03-24\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"IEEE Winter Conference on Applications of Computer Vision. IEEE Winter Conference on Applications of Computer Vision\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/WACV.2014.6836100\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"IEEE Winter Conference on Applications of Computer Vision. IEEE Winter Conference on Applications of Computer Vision","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/WACV.2014.6836100","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Generalized feature learning and indexing for object localization and recognition
This paper addresses a general feature indexing and retrieval scenario in which a set of features detected in the image can retrieve a relevant class of objects, or classes of objects. The main idea behind those features for general object retrieval is that they are capable of identifying and localizing some small regions or parts of the potential object. We propose a set of criteria which take advantage of the learned features to find regions in the image which likely belong to an object. We further use the features' localization capability to localize the full object of interest and its extents. The proposed approach improves the recognition performance and is very efficient. Moreover, it has the potential to be used in automatic image understanding or annotation since it can uncover regions where the objects can be found in an image.