Support vector regression (SVR) is now a well-established method for estimating real-valued functions. However, the standard SVR is not effective to deal with severe outlier contamination of both response and predictor variables commonly encountered in numerous real applications. In this paper, we present a bounded influence SVR, which downweights the influence of outliers in all the regression variables. The proposed approach adopts an adaptive weighting strategy, which is based on both a robust adaptive scale estimator for large regression residuals and the statistic of a "kernelized" hat matrix for leverage point removal. Thus, our algorithm has the ability to accurately extract the dominant subset in corrupted data sets. Simulated linear and nonlinear data sets show the robustness of our algorithm against outliers. Last, chemical and astronomical data sets that exhibit severe outlier contamination are used to demonstrate the performance of the proposed approach in real situations.

Support Vector Regression (SVR) is now a well-established method for estimating real-valued functions. However, the standard SVR is not effective to deal with outliers and structured outliers in training data sets commonly encountered in computer vision applications. In this paper, we present a weighted version of SVM for regression. The proposed approach introduces an adaptive binary function that allows a dominant model from a degraded training dataset to be extracted. This binary function progressively separates inliers from outliers following a one-against-all decomposition. Experimental tests show the high robustness of the proposed approach against outliers and residual structured outliers. Next, we validate our algorithm for object tracking and for optic flow estimation.

Support Vector Regression (SVR) is now a well-established method for estimating real-valued functions. However, the standard SVR is not effective to deal with outliers and structured outliers in training data sets commonly encoutered in computer vision applications. In this paper, we present a weighted version of SVM for regression. The proposed approach introduces an adaptive binary function that allows a dominant model from a degraded training dataset to be extracted. This binary function progressively separates inliers from outliers following a one-against-all decomposition. Experimental tests show the high robustness of the proposed approach against outliers and residual structured outliers. Next, we apply the algorithm to motion estimation in cluttering backgrounds with very encouraging results.

The robust regression is an important tool for the analysis of data contamined by outliers. In computer vision, the optic flow computation is considered as belonging to this kind of problem. In this paper, we discuss a robust optic flow computation based on a modified support vector regression (SVR) technique. We experimentally show that the proposed method significantly improves the robustness against outliers compared to traditional SVR. Next, we illustrate the performances of the method for the optic flow computation problem from noised image sequences. 1