Warped image factor analysis

1st IEEE International Workshop on Computational Advances in Multi-Sensor Adaptive Processing, 2005. Pub Date : 2005-12-13 DOI:10.1109/CAMAP.2005.1574199

Sungjin Hong

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引用次数: 4

Abstract

In factor analysis of sequential data (e.g., time-series or digitized images), the measurement sequence remains "intact" and is assumed to be consistent across all measurement conditions. Otherwise, recovered sequential factors would be distorted. Shifted and warped factor analyses (SFA and WFA) explicitly fit such measurement-sequence inconsistency. Warped image factor analysis (WIFA) combines two ideas: (a) fitting systematic shape variation of image factors, and (b) decomposing many 2D images into a few image factors. WIFA allows image factors to change shape independently, unlike what is assumed in a data-level adjustment: synchronized shape changes of image factors. The latent-level shape variation modeled in WIFA seems to make recovered factors "unique" in some two-way cases, as in SFA and WFA. The shape variation of image factors is parameterized as bilinear warping of segmented images. A quasi-ALS (alternating least squares) algorithm for WIFA is described, which uses alternating regression for factor weights and nonlinear optimization for warping-size parameters. The method is demonstrated with a simulated example

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扭曲图像因素分析

在序列数据(例如，时间序列或数字化图像)的因子分析中，测量序列保持“完整”，并假定在所有测量条件下保持一致。否则，恢复的顺序因子将被扭曲。移位和扭曲因子分析(SFA和WFA)明确适合这种测量序列不一致。扭曲图像因子分析(WIFA)结合了两个思想:(a)拟合图像因子的系统形状变化，(b)将许多二维图像分解为几个图像因子。WIFA允许图像因子独立改变形状，不像在数据级调整中假设的那样:图像因子的形状同步变化。在一些双向的情况下，如在SFA和WFA中，在WIFA中模拟的潜伏水平形状变化似乎使恢复因子“独特”。将图像因子的形状变化参数化为分割图像的双线性翘曲。提出了一种准交替最小二乘(als)算法，该算法采用交替回归法求解因子权重，非线性优化求解翘曲尺寸参数。通过仿真算例对该方法进行了验证

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1st IEEE International Workshop on Computational Advances in Multi-Sensor Adaptive Processing, 2005.

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