TY - GEN

T1 - Shaped local regression and its application to color transforms

AU - Monga, Vishal

AU - Bala, Raja

PY - 2009/12/1

Y1 - 2009/12/1

N2 - Local linear regression is widely used in describing input-output relationships and has been applied with reasonable success to computational problems in color imaging such as approximating printer-models and device color characterization transforms. A popular flavor of local regression is one where locality is achieved by using a weight function which decays as a function of the distance from the regression data point. This paper proposes an improved method for local regression by introducing the notion of "shaping" in the localizing weight function. We make two novel contributions: I) a parameterization of the regression weight function via a shaping matrix, and 2) a method to optimize shape by explicitly introducing the shaping matrix parameters in the regression error measure. Experiments reveal dramatic improvements in approximating printer color transforms by using shaped local linear regression. A particularly pronounced benefit is gained in the case of sparse training sets, which are fairly common in color characterization applications due to the effort and/or cost associated with acquiring color measurements.

AB - Local linear regression is widely used in describing input-output relationships and has been applied with reasonable success to computational problems in color imaging such as approximating printer-models and device color characterization transforms. A popular flavor of local regression is one where locality is achieved by using a weight function which decays as a function of the distance from the regression data point. This paper proposes an improved method for local regression by introducing the notion of "shaping" in the localizing weight function. We make two novel contributions: I) a parameterization of the regression weight function via a shaping matrix, and 2) a method to optimize shape by explicitly introducing the shaping matrix parameters in the regression error measure. Experiments reveal dramatic improvements in approximating printer color transforms by using shaped local linear regression. A particularly pronounced benefit is gained in the case of sparse training sets, which are fairly common in color characterization applications due to the effort and/or cost associated with acquiring color measurements.

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M3 - Conference contribution

AN - SCOPUS:77952665801

SN - 9780892082889

T3 - Final Program and Proceedings - IS and T/SID Color Imaging Conference

SP - 272

EP - 277

BT - 17th Color Imaging Conference

T2 - 17th Color Imaging Conference: Color Science and Engineering Systems, Technologies, and Applications

Y2 - 9 November 2009 through 13 November 2009

ER -