Probabilistic contour mapping using oriented gradient features and SVM-bagging
Shubhra Aich, Yong-Cheol Lee, Chil-Woo Lee · 2015
In this paper, we propose a supervised approach to find out the probabilistic mapping of semantic contours in color images. We prepare a new color image modifying the RGB color planes to incorporate reasonable within-object contrasts in all the color planes. Color gradient based features are then extracted from this altered version of color image. Next, multiple support vector machines (SVMs) are trained with disjoint sets of gradient feature sets. Finally, probabilistic decisions on the test images are made using sigmoid estimation based posterior calculations on the ensemble bagging of SVMs. We demonstrate that this SVM-bagging system is capable of boosting the probability of the pixels near the contour regions compared to that of non-contour ones.