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Proceedings Paper

Joint compression-classification with quantizer/classifier dimension mismatch
Author(s): Naveen Srinivasamurthy; Antonio Ortega
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Paper Abstract

In this paper an algorithm is presented to design encoders that achieve good compression and classification. The goal is to minimize the classification error introduced by quantizing the data using encoders operating on low dimension inputs, which are subsets of the high dimension data used by the classifier for classification. In the encoder design information from the other dimensions of the vector is used to develop efficient encoders which are capable of achieving lower classification error for a given distortion. The design allows a trade-off between distortion and classifications costs providing more flexibility in the overall system design. The algorithm is tested on Gaussian mixture data, which is classified using a classifier which takes as input vectors of quantized values. The proposed technique can trade performance to achieve lower complexity, which is desirable in devices having limited computational resources. For 4 dimensional Gaussian mixture data the misclassification was about 2.2% more than that achieved by using encoders of the same dimension as the classifier, while the encoding complexity was reduced by more than a factor of 2.

Paper Details

Date Published: 29 December 2000
PDF: 12 pages
Proc. SPIE 4310, Visual Communications and Image Processing 2001, (29 December 2000); doi: 10.1117/12.411790
Show Author Affiliations
Naveen Srinivasamurthy, Univ. of Southern California (United States)
Antonio Ortega, Univ. of Southern California (United States)


Published in SPIE Proceedings Vol. 4310:
Visual Communications and Image Processing 2001
Bernd Girod; Charles A. Bouman; Eckehard G. Steinbach, Editor(s)

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