Currently

the efficiency and effectiveness of several existing segmentation algorithms on images with rich texture information are not satisfactory

especially at the object edges between different texture areas. Texture information will disrupt the performance of traditional segmentation algorithms. However

the textures prevail in major images

such as the grass

trees

sky

and sea. The segmentation of texture images without losing edge information becomes crucial. To solve this problem

this paper proposes a novel anisotropic image segmentation algorithm based on continuous texture gradients to reduce the influence of texture areas. An improved watershed(WS) algorithm is proposed to preserve accurate edge data. The benefits of using the WS algorithm are the feature of edge preservation. However

the texture in the different areas of images will reduce the accuracy of the WS algorithm segmentation. Inspired by the traditional anisotropic filtering approaches

this paper presents an improved anisotropic texture gradient

which could detect a different area with a different texture and preserve the sharpness of texture edges. The combination of the WS algorithm and the anisotropic texture gradients maintains the completeness of the edge information among the different texture areas without losing the effect of the segmentation of texture areas. We extend the single-channel color computation of the WS algorithm to three channels(RGB) for improved performance on color image operation. The proposed method maps the altitude information into the continuous texture space by using log function to manage the data sensitivities of texture features. Therefore

the continuous texture space is beneficial for water widget judgment and has good performance on the over-segmentation because of applying the proposed texture anisotropic algorithm in the continuous texture gradient space. We conduct experiments to compare the texture segmentation results with other classical filter operators

such as Gaussian and mean filters and to compare the two newest segmentation approaches on the segmentation accuracy. The datasets used are the BSD500 and the Stanford background datasets

and we select numerous images with rich texture information. After the experiments

the edges filtered by Gaussian and mean filter are all blurred

and thus it is difficult to perform segmentation. However

our proposed anisotropic texture filtering method performs well on edge preservation while removes texture information from object images. Simultaneously

the results indicated that the proposed algorithm has obvious advantages on accuracy and edge conservation compared with the newest approaches. We compute the average accuracy of segmentation to evaluate the results quantitatively. The average accuracy of our method has reached nearly 90.9%

obviously exceeding other recent algorithms. This result verifies the effectiveness of our method. The proposed algorithm has advantages of edge preserving and high accuracy with less over-segmentation. The feasibility of the algorithm is proven through comparison with the latest segmentation algorithms. Our method is suitable for images with rich texture information

such as natural and artificial textures.