Graph-based image segmentation using weighted color patch

Q: How do the authors calculate the local weights of pixel gr?

the local weight of pixel gr is obtained by normalizing the value of ξr:ωr = ξr ∑r∈Nk ξr(3)Since the applied Gaussian kernel decays very fast, those pixels whose mean-square deviation is far away from the average value will have a relatively small weights.

Q: What is the proposed method for calculating the weight of edges in the affinity graph?

For image segmentation, the authors apply the proposed method to compute the weight of edges in the affinity graph, which is further partitioned by the normalized cut (NCut) algorithm [19].

Q: How many distances between the object and the background?

Without the global weights, the distances between the background and the objects are 55 and 155 respectively, while both distances become 158.5 when considering the global weights.

Question

1. What contributions have the authors mentioned in the paper "Graph-based image segmentation using weighted color patch" ?

2. How do the authors incorporate spatial information into the affinity graph?

3. What are the advantages of the weighted color patch method?

4. What is the common feature used to construct a graph?

Accepted Answer

In this paper, the authors propose a new method based on the weighted color patch to compute the weight of edges in an affinity graph.. The authors evaluate the proposed method on the Prague color texture image benchmark and the Berkeley image segmentation database.. Furthermore, the authors assign both local and global weights adaptively for each pixel in a patch in order to alleviate the over-smooth effect of using patches.

Accepted Answer

in order to incorporate spatial information, the authors also propose to assign a global weight to each pixel in an image according to different proportion of the object and background, so that the contrast between them is enhanced and a more discriminative affinity graph is constructed.

Accepted Answer

There are two main advantages: i) it can smooth local regions by averaging color information and ii) it can capture texture information by considering context neighboring cue.

Accepted Answer

the graph-based methods first construct an affinity graph from a given image, and then partition the resulting graph into different clusters with certain cut criterions [10] [19].

Accepted Answer

the features applied to the graph construction can be categorized as pixel-based and region-based according to the definition of graph nodes.

Accepted Answer

In addition to the local weights, which only reflect the structure of a local patch, the authors also propose to assign a global weight to each pixel in an image according to different proportion of the object and background, since the authors observed that they should have different contribution to the affinity graph construction because of different structure of the whole image content.

Accepted Answer

In order to incorporate color information, the affinity graph is first computed in each channel of the RGB color space, formally defined as follows:W (xi, xj) = e −(‖∑N i=1 P c wi− ∑N j=1 P c wj‖2/σ), ||xi − xj||2 < r (4)where W is the affinity graph, and W (i, j) defines the edge weight of two vertices i and j in the graph.

Accepted Answer

the local weight of pixel gr is obtained by normalizing the value of ξr:ωr = ξr ∑r∈Nk ξr(3)Since the applied Gaussian kernel decays very fast, those pixels whose mean-square deviation is far away from the average value will have a relatively small weights.

Accepted Answer

For image segmentation, the authors apply the proposed method to compute the weight of edges in the affinity graph, which is further partitioned by the normalized cut (NCut) algorithm [19].

Accepted Answer

Without the global weights, the distances between the background and the objects are 55 and 155 respectively, while both distances become 158.5 when considering the global weights.

Accepted Answer

feature is an important factor since its properties directly influence the discriminative power of the resulting affinity graph.

Accepted Answer

Color and contour cue [7] are often combined in an unsupervised manner, and some works tried to fuse multiple types of features by learning on the ground-truth data of image database [4].

Accepted Answer

Given an image I, it can be represented as a graph G = (V,E), with V being the set of vertices and E being the set of edges connecting two vertices.

Accepted Answer

The Berkeley image database contains 300 images and their corresponding ground truth (each image has at least 4 human annotations).

Accepted Answer

This work was supported by the Chinese Scholarship Council (CSC), and by the French research agency ANR through the VideoSense project under the grant 2009 CORD 026 02.

Accepted Answer

Fig.4 presents some visual comparisons of their results with the other methods, and the authors can see that NCut tends to splithomogenous large region into separate regions and GBIS has thick edges, while their proposed method can obtain more meaningful region with accurate boundary.

Graph-based image segmentation using weighted color patch

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Most frequently asked questions

1. What contributions have the authors mentioned in the paper "Graph-based image segmentation using weighted color patch" ?

2. How do the authors incorporate spatial information into the affinity graph?

3. What are the advantages of the weighted color patch method?

4. What is the common feature used to construct a graph?

5. What is the definition of graph nodes?

6. How do the authors assign weights to each pixel in an image?

7. What is the affinity graph for a color space?

8. How do the authors calculate the local weights of pixel gr?

9. What is the proposed method for calculating the weight of edges in the affinity graph?

10. How many distances between the object and the background?

11. What is the importance of feature in graph-based methods?

12. How are the two types of features used in graph-based methods?

13. What is the graph of the image?

14. How many images are in the Berkeley image database?

15. What is the funding for this work?

16. What is the difference between the two methods?

Figures

Citations

An unsupervised segmentation method based on dynamic threshold neural P systems for color images

Solving the Max-3-Cut Problem with Coherent Networks

Graph based approaches for image segmentation and object tracking

Normalized Cut optimization based on color perception findings. A comparative study

Clustering and Graph Convolution of Sub-Regions for Unsupervised Image Segmentation

References

Normalized cuts and image segmentation

Normalized cuts and image segmentation

A tutorial on spectral clustering

A non-local algorithm for image denoising

A database of human segmented natural images and its application to evaluating segmentation algorithms and measuring ecological statistics

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