Srilekha Mudumbai, K. Shah, A. Sheth, Krishnan Parasuraman, C. Bertram
{"title":"斑马图像存取系统","authors":"Srilekha Mudumbai, K. Shah, A. Sheth, Krishnan Parasuraman, C. Bertram","doi":"10.1109/ICDE.1998.655826","DOIUrl":null,"url":null,"abstract":"The ZEBRA system, which is part of the VisualHarness platform for managing heterogeneous data, supports three types of access to distributed image repositories: keyword based, attribute based, and image content based. A user can assign different weights (relative importance) to each of the three types, and within the last type of access, to each of the image properties. The image based access component (IBAC) supports access based on computable image properties such as those based on spatial domain, frequency domain or statistical and structural analysis. However, it uses a novel black box approach of utilizing a Visual Information Retrieval (VIR) engine to compute corresponding metadata that is then independently managed in a relational database to provide query processing involving image features and information correlation. That is, one overcomes the difficulties in using the feature vectors that are proprietary to a VTR engine, as one does not require any knowledge of the internal representation or format of the image feature used by a VIR engine.","PeriodicalId":264926,"journal":{"name":"Proceedings 14th International Conference on Data Engineering","volume":"34 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"1998-02-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"ZEBRA image access system\",\"authors\":\"Srilekha Mudumbai, K. Shah, A. Sheth, Krishnan Parasuraman, C. Bertram\",\"doi\":\"10.1109/ICDE.1998.655826\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"The ZEBRA system, which is part of the VisualHarness platform for managing heterogeneous data, supports three types of access to distributed image repositories: keyword based, attribute based, and image content based. A user can assign different weights (relative importance) to each of the three types, and within the last type of access, to each of the image properties. The image based access component (IBAC) supports access based on computable image properties such as those based on spatial domain, frequency domain or statistical and structural analysis. However, it uses a novel black box approach of utilizing a Visual Information Retrieval (VIR) engine to compute corresponding metadata that is then independently managed in a relational database to provide query processing involving image features and information correlation. That is, one overcomes the difficulties in using the feature vectors that are proprietary to a VTR engine, as one does not require any knowledge of the internal representation or format of the image feature used by a VIR engine.\",\"PeriodicalId\":264926,\"journal\":{\"name\":\"Proceedings 14th International Conference on Data Engineering\",\"volume\":\"34 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"1998-02-23\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Proceedings 14th International Conference on Data Engineering\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/ICDE.1998.655826\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Proceedings 14th International Conference on Data Engineering","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ICDE.1998.655826","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
The ZEBRA system, which is part of the VisualHarness platform for managing heterogeneous data, supports three types of access to distributed image repositories: keyword based, attribute based, and image content based. A user can assign different weights (relative importance) to each of the three types, and within the last type of access, to each of the image properties. The image based access component (IBAC) supports access based on computable image properties such as those based on spatial domain, frequency domain or statistical and structural analysis. However, it uses a novel black box approach of utilizing a Visual Information Retrieval (VIR) engine to compute corresponding metadata that is then independently managed in a relational database to provide query processing involving image features and information correlation. That is, one overcomes the difficulties in using the feature vectors that are proprietary to a VTR engine, as one does not require any knowledge of the internal representation or format of the image feature used by a VIR engine.