Paper
13 March 1996 Efficiency issues related to probability density function comparison
Patrick M. Kelly, T. Michael Cannon, Julio E. Barros
Author Affiliations +
Proceedings Volume 2670, Storage and Retrieval for Still Image and Video Databases IV; (1996) https://doi.org/10.1117/12.234808
Event: Electronic Imaging: Science and Technology, 1996, San Jose, CA, United States
Abstract
The CANDID project (comparison algorithm for navigating digital image databases) employs probability density functions (PDFs) of localized feature information to represent the content of an image for search and retrieval purposes. A similarity measure between PDFs is used to identify database images that are similar to a user-provided query image. Unfortunately, signature comparison involving PDFs is a very time-consuming operation. In this paper, we look into some efficiency considerations when working with PDFs. Since PDFs can take on many forms, we look into tradeoffs between accurate representation and efficiency of manipulation for several data sets. In particular, we typically represent each PDF as a Gaussian mixture (e.g. as a weighted sum of Gaussian kernels) in the feature space. We find that by constraining all Gaussian kernels to have principal axes that are aligned to the natural axes of the feature space, computations involving these PDFs are simplified. We can also constrain the Gaussian kernels to be hyperspherical rather than hyperellipsoidal, simplifying computations even further, and yielding an order of magnitude speedup in signature comparison. This paper illustrates the tradeoffs encountered when using these constraints.
© (1996) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Patrick M. Kelly, T. Michael Cannon, and Julio E. Barros "Efficiency issues related to probability density function comparison", Proc. SPIE 2670, Storage and Retrieval for Still Image and Video Databases IV, (13 March 1996); https://doi.org/10.1117/12.234808
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Cited by 10 scholarly publications.
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KEYWORDS
Databases

Image retrieval

Distance measurement

Feature extraction

Digital imaging

Matrices

Agriculture

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