外文翻譯---特征空間穩(wěn)健性分析彩色圖像分割

1、附錄 附錄 2：外文翻譯 ：外文翻譯Robust Analysis of Feature Spaces: Color Image SegmentationAbstractA general technique for the recovery of significant image features is presented. The technique is based on the mean shift algorithm, a

2、simple nonparametric procedure for estimating density gradients. Drawbacks of the current methods (including robust clustering) are avoided. Feature space of any nature can be processed, and as an example, color image se

3、gmentation is discussed. The segmentation is completely autonomous, only its class is chosen by the user. Thus, the same program can produce a high quality edge image, or provide, by extracting all the significant colors

4、, a preprocessor for content-based query systems. A 512 512 color image is analyzed in ?less than 10 seconds on a standard workstation. Gray level images are handled as color images having only the lightness coordinate.

5、Keywords: robust pattern analysis, low-level vision, content-based indexinginformation must be provided by the user, and for multimodal distributions it is difficult to guess the optimal setting.Nevertheless, a reliable

6、general technique for feature space analysis can be developed using a simple nonparametric density estimation algorithm. In this paper we propose such a technique whose robust behavior is superior to methods employing ro

7、bust estimators from statistics.2 Requirements for RobustnessEstimation of a cluster center is called in statistics the multivariate location problem. To be robust, an estimator must tolerate a percentage of outliers, i.

8、e., data points not obeying the underlying distribution of the cluster. Numerous robust techniques were proposed, and in computer vision the most widely used is the minimum volume ellipsoid (MVE) estimator proposed by Ro

9、usseeuw.The MVE estimator is affine equivariant (an affine transformation of the input is passed on to the estimate) and has high breakdown point (tolerates up to half the data being outliers). The estimator finds the ce

10、nter of the highest density region by searching for the minimal volume ellipsoid containing at least h data points. The multivariate location estimate is the center of this ellipsoid. To avoid combinatorial explosion a p

11、robabilistic search is employed. Let the dimension of the data be p. A small number of (p+1) tuple of points are randomly chosen. For each (p+1) tuple the mean vector and covariance matrix are computed, defining an ellip

12、soid. The ellipsoid is inated to include h points, and the one having the minimum volume provides the MVE estimate.Based on MVE, a robust clustering technique with applications in computer vision was proposed in. The dat

13、a is analyzed under several \resolutions“ by applying the MVE estimator repeatedly with h values representing fixed percentages of the data points. The best cluster then corresponds to the h value yielding the highest de

14、nsity inside the minimum volume ellipsoid. The cluster is removed from the feature space, and the whole procedure is repeated till the space is not empty. The robustness of MVE should ensure that each cluster is associat

15、ed with only one mode of the underlying distribution. The number of significant clusters is not needed a priori.The robust clustering method was successfully employed for the analysis of a large variety of feature spaces