Efficient Access based on the Feature Origin

People

Jelena Tesic, Sitaram Bhagavathy, B.S.Manjunath

Objective

In typical applications, image/video descriptors are of high dimensionality (from tens to several hundreds). Color, texture, shape and motion descriptors are some of the commonly used low-level visual descriptors for image and video data. For example, a 256-dimensional color histogram descriptor is often used to characterize the color distribution in a given image. The feature space grows exponentially with the dimensions, and the search complexity increases at the same rate. In high feature dimensions, the curse of dimensionality is an issue as the traditional database indexing methods and clustering methods do not scale well beyond 10--20 dimensions. This poses challenging problems for database access. Therefore, the high dimensionality and computational complexity of this descriptor adversely affect the efficiency of content-based retrieval systems.

We propose a modified MPEG7 texture descriptor that has comparable performance, but with nearly half the dimensionality and less computational expense. Furthermore, it is easy to compute the new feature using the old one, without having to repeat the computationally expensive filtering step. We also propose a new normalization adaptive indexing methods that improve similarity retrieval and a bit allocation compression scheme for indexing that improves search efficiency up to 10 times. New descriptor and indexing structure are evaluated over range of scientific datasets.

Publications

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Acknowledgements

This research was supported in part by The Institute of Scientific Computing Research (ISCR) award under the auspices of the U.S. Department of Energy by the Lawrence Livermore National Laboratory under contract No.W-7405- ENG-48, ONR# N00014-01-1-0391, NSF Instrumentation #EIA-9986057, and NSF Infrastructure #EIA-0080134.