ZEBRA image access system

Srilekha Mudumbai, K. Shah, A. Sheth, Krishnan Parasuraman, C. Bertram
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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.
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斑马图像存取系统
ZEBRA系统是VisualHarness平台的一部分,用于管理异构数据,它支持三种对分布式图像存储库的访问:基于关键字的、基于属性的和基于图像内容的。用户可以为这三种类型中的每一种分配不同的权重(相对重要性),并在最后一种访问类型中为每个图像属性分配不同的权重。基于图像的访问组件(IBAC)支持基于可计算图像属性的访问,例如基于空间域、频率域或统计和结构分析的图像属性。然而,它使用了一种新颖的黑盒方法,利用视觉信息检索(VIR)引擎来计算相应的元数据,然后在关系数据库中独立管理,以提供涉及图像特征和信息相关性的查询处理。也就是说,人们克服了使用VTR引擎专有的特征向量的困难,因为人们不需要了解VIR引擎使用的图像特征的内部表示或格式。
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