Image segmentation by integrating multiple channels of features
Authors: Ronald Chi-Kit Chung
Venue:  (2008)

Image segmentation is about how an image could be divided into pieces or segments so that each segment corresponds to a surface or an object which demonstrates high degree of uniformity in its visual appearance. Automatic image segmentation method must in a way make use of the uniformity property of the segments. The uniformity property of a segment however manifests in a number of ways, forming different channels of features. Since the segment's appearance is uniform within itself, the intensities or textures in the image of the segment must look rather similar, and they together are what the literature calls the region-level features. Since a segment's appearance uniformity must be different from those of the immediate neighboring segments, or else they should not be referred to as different segments, the boundary of a segment therefore should exhibit high degree of intensity contrast in the image data, and such contrast leads to edgel features (which is often referred to as boundary features) in the image data. Other channels of features include discrete labels assigned to image pixels according to their intensity levels, and certain prior knowledge of the object shape in the image. Much of the previous work on image segmentation is based upon features of a particular channel, such as the edgel features, the region features, or others. The objective of this thesis is to explore how features of multiple channels can be put together integratively, or more precisely in an active contour deformation process under the level set formulation, for more accurate image segmentation. Three combinations of features are investigated. The first is about integrating the boundary features and region features. The second is about integrating the boundary features and the labels to pixels according to certain coarse intensity clustering. The third is about integrating the boundary features and certain prior knowledge of the object shape. In the first piece of work, an approach of gray level image segmentation is investigated, which uses boundary feature and region feature complementarity. In this approach, the line segments, which are derived by grouping edge elements, are used to construct a saliency map to indicate the location likelihood of the real boundaries. The closed boundaries extracted from the saliency map are then refined by a region based active contour method. The scheme allows the challenging issues of boundary closure and segmentation accuracy to be both addressed. In the second piece of work, an approach of foreground-background segmentation is explored, which integrates the boundary features and certain labels assigned to the image pixels according to their intensity levels. The labels are in accordance with certain coarse clustering over the intensity histogram of the image. In this approach, an inhomogeneity measure is encoded in a variational formulation, thus the measure can be applied to the entire image domain and be made global. The approach has a uniform treatment to gray level, color and texture images. In addition, the approach allows explicit encouragement on the smoothness of the segmentation boundary by using the level set technique-based active contour method. In the third piece of work, an approach of foreground-background segmentation is investigated, that makes use of both the boundary features and certain prior knowledge of object shape. This approach can also be categorized as an object detection method. In this approach, we adopt a new multiplicative formulation to combine the edgel information and the prior shape knowledge. The method reduces the number of system parameters and increases the algorithm's robustness. The proposed algorithms in this thesis have been tested on many real and synthetic images. The experimental results illustrate their efficacy and limitation.