Objective

2

Superpixel segmentation is widely used as a preprocessing step in many computer vision applications. It groups the pixels of an image into homogeneous regions while trying to respect the object boundary. Generally

a good superpixel segmentation method would meet the following three conditions. First

the boundaries of the superpixel should adhere well to the image boundaries. Second

the boundaries of the superpixel should not wing across different objects in the image. Third

superpixels should have similar sizes and regular shapes. In recent years

various superpixel segmentation methods have been proposed; however

most of these state-of-the-art methods only use the pixel information as a clustering feature. Therefore

they can be severely impacted by high-frequency contrast variations and fail to produce equally sized regions having the same texture. To make superpixels robust to contrast variations such as strong gradient texture

we propose a texture-aware superpixel segmentation algorithm that uses patch-level features for clustering purposes.

Method

2

The main idea of our algorithm is to calculate the color distance by using a specially designed quarter-circular mean filtering operator. Because the mean filtering has the characteristics of noise suppression and texture smoothing and the rotated quarter-circular window ensures that the mean filtering sampled pixels are located inside the superpixels as much as possible

the quarter-circular mean filtering operator has the capacity to identify the texture pattern. The Sobel gradient has the advantages of fast speed and thin edge

but it is easy to be disturbed by strong gradient texture. The interval gradient is characterized by texture suppression and structure preservation

but its edge is too thick. To overcome their shortcomings while retaining their strengths

we devise a hybrid gradient based on the multiplication of the Sobel gradient and interval gradient

which has the advantages of texture suppression

structure preservation

and edge thinning; therefore

its magnitude can represent the possibility that a pixel belongs to the structure. Finally

an integrated structure-avoiding clustering distance is proposed by looking for the maximum hybrid gradient magnitude along the linear path

which can further enhance the boundary adherence of the superpixels.

Result

2

To verify the universality of our algorithm

we test the Berkeley segmentation dataset (BSDS500)

which contains 500 images of indoor

outdoor

human

animal

and other scenes with five manually ground truths. To verify the particularity of our algorithm

we test two mosaic images with strong gradient texture. Unfortunately

these two mosaic images do not have ground truth

and they can only be evaluated subjectively by the human eye. All experiments of our algorithm are run on the Windows platform

which requires the mixed programming of MATLAB and Visual studio. Two main parameters need to be set in our algorithm

namely

the mean filtering window radius and the interval gradient operator radius. As both of them aim to capture the regularity of the texture

we set them the same value. Furthermore

the size of the window radius depends on the texture size; the larger the texture size

the larger the window radius required. Normally

we suggest taking the window radius between 3 and 5. We compare our algorithm with other popular superpixel segmentation methods

and all methods use the code with the optimal parameters provided by the authors to obtain their superpixel segmentation results. The superpixel segmentation performance is tested and judged in terms of the boundary recall

undersegmentation error (UE)

achievable segmentation accuracy (ASA)

and compactness measure (CM). By testing on the BSDS500 image dataset

our algorithm obtains a 1.5% lower UE value

0.2% higher ASA value

and 4.3% higher CM value. By testing on many mosaic images with strong gradient textures

our algorithm generates superpixels with not only regular shapes but also better boundary adherence. The experimental results show that our algorithm surpasses the state-of-the-art methods in superpixel segmentation performance on BSDS500 and mosaic images.

Conclusion

2

To make superpixel segmentation robust to high-frequency contrast variations such as strong gradient texture and noise

we propose a texture-aware superpixel segmentation algorithm

which mainly contributes in the following three aspects. First

we design a quarter-circular mean filtering operator

which is sensitive to the texture pattern. Second

we bring forward a hybrid gradient based on the multiplication of the Sobel gradient and interval gradient

which can distinguish between texture and structure pixels. Third

an integrated structure-avoiding clustering distance is devised based on the hybrid gradient magnitude. It aims to prevent the superpixels from crossing the structure boundary and keep the superpixels with regular size. The experimental results show that our algorithm performs equally well or better than state-of-the-art superpixel segmentation methods in terms of the commonly used evaluation metrics. In the face of strong gradient textures

our method can generate superpixels with regular shape and better boundary adherence. Thus

our superpixel segmentation algorithm has great potential in target recognition

target tracking

and significance detection.