Black Box approach to Visual Image Manipulation used by Visual Information Retrieval Engines

TitleBlack Box approach to Visual Image Manipulation used by Visual Information Retrieval Engines
Publication TypeConference Paper
Year of Publication1997
AuthorsK. Shah, Amit Sheth, S. Mudumbai
Conference NameBlack Box approach to Visual Image Manipulation used by Visual Information Retrieval Engines
Conference LocationPortland, OR, USA
Abstract

The Zebra image access system 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. The Image based access component (IBAC) of Zebra supports the last type of 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, we overcome the difficulties in using the feature vectors that are proprietary to a VIR engine, as we do not require any knowledge of the internal representation or format of the image feature used by a VIR engine. IBAC also gives the user an option of combining any of the image properties. Moreover a user can assign different weights (relative importance) to each of the image properties so that the query results can be returned to the user depending on the weights assigned for different properties. Tests focusing on the quality of the results obtained using the black box approach, when compared to the results obtained by the VIR used by the black box approach, are encouraging. These are briefly presented and are also accessible on the Web-accessible prototype system.

Full Text

K. Shah, A. Sheth, and S. Mudumbai, “Black Box approach to Visual Image Manipulation used by Visual Information Retrieval Engines,” in Proceedings of 2nd IEEE Metadata Conference, September 1997. (Proceedings primarily on the Web, no page numbers).

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