A New Tabular Algorithm for Rapid Vector Quantization Encoding and Nearest Neighbor Classification

Award Information
Agency:
Department of Defense
Branch
Missile Defense Agency
Amount:
$59,613.00
Award Year:
1995
Program:
SBIR
Phase:
Phase I
Contract:
n/a
Award Id:
28413
Agency Tracking Number:
28413
Solicitation Year:
n/a
Solicitation Topic Code:
n/a
Solicitation Number:
n/a
Small Business Information
4141 Colonel Glenn Hwy, Suite, 140, Beavercreek, OH, 45431
Hubzone Owned:
N
Minority Owned:
N
Woman Owned:
N
Duns:
n/a
Principal Investigator:
Gary Key
(513) 429-3302
Business Contact:
() -
Research Institute:
n/a
Abstract
We offer an approach to a multiple-order-of-magnitude processing speed/efficiency improvement in vector quantization (VQ) data/image compression, automatic target recognition/classification, and time signal/signature classification. We base our approach on an algorithmic breakthrough, the TABULAR NEAREST-NEIGHBOR ENCODER (TNE), which we discovered and partially developed last year. This algorithm reduces the computational complexity associated with such problems from 1 to over 4 orders-of magnitude compared to other techniques. A significant property is the relative insensitivity of the algorithm's complexity to codebook and/or vector partition size. This property renders feasible the use of very large image quantization or feature vector codebooks in real-time with relatively modest processor hardware. Larger codebooks will render practicable larger vector partitionings and thence greater data compression ratios than were previously associated with VQ methods. The TNE architecture is well suited for parallel processing and for independent sub-space (i.e., correlated partial image block) classifications. This latter property will greatly facilitate model-based classification of partially obscured target imagery and partially masked time signatures. This effort will significantly advance the state-of-the-art in real-time image/data compression; in automatic recognition/classification of exposed and/or partially obscured targets; and in related commercial applications (multimedia, video compression, speech recognition/compression/synthesis, etc.)

* information listed above is at the time of submission.

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