1、附录 2：外文翻译 Robust Analysis of Feature Spaces: Color Image Segmentation Abstract A general technique for the recovery of significant image features is presented. The technique is based on the mean shift algorithm, a simple nonparametric procedure for estimating density gradients. Drawbacks of the curr
2、ent methods (including robust clustering) are avoided. Feature space of any nature can be processed, and as an example, color image segmentation 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
3、, or provide, by extracting all the significant colors, 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. Keywords: robust pattern an
4、alysis, low-level vision, content-based indexing 1 Introduction Feature space analysis is a widely used tool for solving low-level image understanding tasks. Given an image, feature vectors are extracted from local neighborhoods and mapped into the space spanned by their components. Significant feat
5、ures in the image then correspond to high density regions in this space. Feature space analysis is the procedure of recovering the centers of the high density regions, i.e., the representations of the significant image features. Histogram based techniques, Hough transform are examples of the approac
6、h. When the number of distinct feature vectors is large, the size of the feature space is reduced by grouping nearby vectors into a single cell. A discretized feature space is called an accumulator. Whenever the size of the accumulator cell is not adequate for the data, serious artifacts can appear.
7、 The problem was extensively studied in the context of the Hough transform, e.g. Thus, for satisfactory results a feature space should have continuous coordinate system. The content of a continuous feature space can be modeled as a sample from a multivariate, multimodal probability distribution. Not
8、e that for real images the number of modes can be very large, of the order of tens. The highest density regions correspond to clusters centered on the modes of the underlying probability distribution. Traditional clustering techniques, can be used for feature space analysis but they are reliable onl
9、y if the number of clusters is small and known a priori. Estimating the number of clusters from the data is computationally expensive and not guaranteed to produce satisfactory result. A much too often used assumption is that the individual clusters obey multivariate normal distributions, i.e., the
10、feature space can be modeled as a mixture of Gaussians. The parameters of the mixture are then estimated by minimizing an error criterion. For example, a large class of thresholding algorithms are based on the Gaussian mixture model of the histogram, e.g. However, there is no theoretical evidence th
11、at an extracted normal cluster necessarily corresponds to a significant image feature. On the contrary, a strong artifact cluster may appear when several features are mapped into partially overlapping regions. Nonparametric density estimation avoids the use of the normality assumption. The two families of methods, Parzen window, and k-nearest neighbors, both require additional input information (type of the kernel, number of neighbors). This
