1 January 2005 Fast indexing and searching strategies for feature-based image database systems
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J. of Electronic Imaging, 14(1), 013019 (2005). doi:10.1117/1.1866148
Abstract
Because visual data require a large amount of memory and computing power for storage and processing, it is greatly desired to efficiently index and retrieve the visual information from image database systems. We propose efficient indexing and searching strategies for feature-based image database systems, in which uncompressed and compressed domain image features are employed. Each query or stored image is represented by a set of features extracted from the image. The weighted square sum error distance is employed to evaluate the ranks of retrieved images. Many fast clustering and searching techniques exist for the square sum error distance used in vector quantization (VQ), in which different features have identical weighting coefficients. In practice, different features may have different dynamic ranges and different importances, i.e., different features may have different weighting coefficients. We derive a set of inequalities based on the weighted square sum error distance and employ it to speed up the indexing (clustering) and searching procedures for feature-based image database systems. Good simulation results show the feasibility of the proposed approaches.
Li-Wei Kang, Jin-Jang Leou, "Fast indexing and searching strategies for feature-based image database systems," Journal of Electronic Imaging 14(1), 013019 (1 January 2005). http://dx.doi.org/10.1117/1.1866148
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KEYWORDS
Databases

Image retrieval

Image processing

Image compression

Distance measurement

Feature extraction

Quantization

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