Gabor Analysis is frequently used for texture analysis and segmentation. Once the Gaborian feature space is generated it may be interpreted in various ways for image analysis and segmentation. Image segmentation can also be obtained via the application of "snakes" or active contour mechanism, which is usually used for gray-level images. In this study we apply the active contour method to the Gaborian feature space of images and obtain a method for texture segmentation. We calculate six localized features based on the Gabor transform of the image. These are the mean and variance of the localized frequency, orientation and intensity. This feature space is presented, via the Beltrami framework, as a Riemannian manifold. The stopping term, in the geodesic snakes mechanism, is derived from the metric of the features manifold. Experimental results obtained by application of the scheme to test images are presented.