摘要
Dimension reduction and manifold learning are the two most popular feature extraction methods.The two methods focus on spatial locality as a guiding principle to find a low-dimensional basis for describing high-dimensional data,but no bases or features are more spatially localized than the original image pixels.So,adaptive image combination is presented to represent a class by a combined sample.The combined sample is a linear combination of original samples in the same class.Adaptive image combination (AIC) find the best combination coefficients by minimizing the intrapersonal distance and maximizing the interpersonal distance.Experimental results show that AIC is effective.
Dimension reduction and manifold learning are the two most popular feature extraction methods. The two methods focus on spatial locality as a guiding principle to find a low-dimensional basis for describing high-dimensional data, but no bases or features are more spatially localized than the original image pixels. So, adaptive image combination is presented to represent a class by a combined sample. The combined sample is a linear combination of original samples in the same class. Adaptive image combination （AIC） find the best combination coefficients by minimizing the intrapersonal distance and maximizing the interpersonal distance. Experimental results show that AIC is effective.
基金
the Science and Technology Program of Shanghai Maritime University (Nos.20100095,20100068 and 20080474)
the Innovation Program of Shanghai Municipal Education Commission (No.11ZZ143)
