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Figure 4: Segmentation results of our algorithm on images of Berkeley database. The same images as in [14] are chosen, to allow a direct comparison to one of the state-of-the-art algorithms. Based on the GCE we have compared our results to six state-of-the-art color segmentation algorithms: the JSEG algorithm from Deng and Manjunath (Jseg) [7], the Mean Shift method (Mshift) from Comaniciu and Meer [3], the standard normalized cut algorithm (Ncuts) from Shi and Malik [17], the multi-scale normalized cut approach (Mscuts) from Cour et al. [5], the seeded graph cuts method (Seed) from Micusik and Hanbury [14] and the pixel affinity based method (Affin) from Fowlkes et al. [10]. All results were calculated with publicly available implementations, with the exception of Affin and Seed, where the results are taken directly from the paper [14]. At least five human segmentations, which represent the ground truth for this benchmark, are available for each of the 200 images from the Berkeley database. In the first step, for every algorithm 200 mean GCE values denoted as GCE n , with n = 1 . . . 200, were calculated by GCE n = 1 ∑H n GCE ( Seg n , Hum i H n) , (18) n i=1 where H n is the number of available human segmentations for the n-th image, Seg n is the segmentation result of one of the seven algorithm and Hum i n is the i-th human segmentation of the n-th image. Then, for each algorithm an overall quality measure GCE was calculated by GCE = 1 ∑200 GCE n . (19) 200 n=1 To be able to measure the consistency of the human segmentations itself, we computed GCE values of all possible combinations between all available human segmentations for every image, which additionally allows calculation of an overall mean GCE for the human results. The obtained value was 0.0783, which is an indication for good consistency of the human segmentations. Table 1 summarizes the comparison of the aforementioned algorithms using the quality measure GCE. As one can see, Ncuts and Mscuts exhibit the highest values indicating poorer performance compared to the other algorithms, while our method yields the lowest value. In addition, our algorithm provides on average 18 regions per segmentation, which is close to the human average. To further compare the performance of different algorithms, many publications, as e. g. [14] calculate the GCEs to all available human segmentations and summarize these values in a histogram, as shown in Figure 5. Again, it seems that the algorithms approximately perform the same, except the Ncuts and the Mscuts algorithms, their histograms are located more at the right side. The histograms shown in Figure 5 do not allow a deeper insight into divergences between segmentation results obtained with different methods, since the correspondence between the calculated GCE and the input image is lost. In order to overcome this weakness, we compare our approach directly to other algorithms using scatter plots, as shown in Figure 6. Each point in these plots represents one of the 200 input images, where the x-value is the GCE n of our method and the y-value is the GCE n of the algorithm currently compared. Points on the diagonal line y = x indicate identical deviation from the ground truth for both algorithms, while the distance to the line is a measure for the disagreement between the obtained segmentation results.
Human Jseg Mshift Ncuts GCE Reference 0.1996 0.1870 0.2811 # regions 17 36 20 7 Mscuts Affin Seed Our GCE 0.2557 0.214 0.209 0.1840 # regions 7 13 4 18 Table 1: Comparison of state-of-the art methods. It shows the quality measure GCE and the average number of detected regions. The GCE values for the Affin and Seed method are taken directly from [14]. (a) Jseg vs. ROI-SEG (b) Mshift vs. ROI-SEG (a) Human vs. Human (b) Human vs. Jseg (c) Human vs. Mshift (c) Mscuts vs. ROI-SEG (d) Ncuts vs. ROI-SEG (d) Human vs. Ncuts (e) Human vs. Mscuts (f) Human vs. Our Figure 5: Comparison of state-of-the-art algorithms by histograms showing the GCE distribution computed on 200 Berkeley images. Points above the diagonal line represent images, where our algorithm performs better, whereas points below indicate superior performance of the other algorithm. This discrimination is emphasized by different colors in the scatter plots. As can be seen in Figures 6(c) and 6(d), the two normalized cut based approaches perform inferior to our approach, since most of the points are above the diagonal line. Although the Jseg algorithm has a almost similar GCE, the scatter plot in Figure 6(a) reveals that the points above the diagonal line are on average farther away from the line than the points below. This is an indication that for some images Jseg performs distinctively poorer as our approach. Mshift in Figure 6(b) achieved almost similar performance as our algorithm. In order to gain a deeper insight into the main differences between these two algorithms, it is essential to identify images depicting scenes which cause different, respectively the same, levels of performance. The required information is available in the scatter plots, because every point can be matched to the corresponding image. Therefore, the scatter plot from Figure 6(b) is analyzed in more detail in Figure 7, which shows the corresponding images of key data points located in the four corners of the plot. The data points in the lower left corner of Figure 7 represent images, where both algorithms achieved almost sim- Figure 6: Comparison of segmentation performance using GCE scatter plots. Points above the diagonal line represent images, where ROI-SEG performs better. This discrimination is emphasized by different colors in the scatter plots. ilar agreement with the ground truth. The three key images shown, reveal that these are images, which can be easily segmented in distinct regions. The upper right corner contains images, where both results exhibit a large difference to the ground truth. These are images with hard-to-segment, complex scenes. On the other hand, the upper left and the lower right images are the most interesting ones, because they cause a large difference in the performance of the two algorithms. As can be seen in Figure 6(b), there is a larger number of data points in the upper left corner compared to the lower right, which indicates that there are more images, where our algorithm performs distinctively better. But in contrast, for the easy-to-segment images in the lower left corner, the Mshift mostly performs better, because its detected region boundaries are more accurate than ours. To sum up, our ROI-SEG algorithm shows competitive results to state-of-the-art segmentation algorithms. Additionally, because of the efficient design of the individual concepts our algorithm provides results in short computation times, outperforming the other algorithms. The detection of the input ROIs is done within a second and every sub-segmentation needs about half a second for the Berkeley images. Thus, segmentation results can be achieved in a few seconds.
Page 1 and 2: ROI-SEG: Unsupervised Color Segment
Page 3 and 4: The Mean Shift algorithm proposed b
Page 5: (a) Nine ROIs (b) Seven ROIs (c) Ei

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