Supplementary Material for “Blending Texture Features from Multiple ReferenceImages for Style Transfer ”
Hikaru Ikuta1, 2∗
1

Keisuke Ogaki2†

The University of Tokyo

2

DWANGO Co., Ltd.

Figure 1: Relationship between the number of reference images
and output image quality. Each row shows the style transfer results
for the same author, and each column shows the number of reference images used for style transfer. The number of reference images
used in each columns are 1, 5, 10, 30, and 50 images, starting fromthe left.

1

Relationship Between the Number of Reference Images and Output Quality

Here we study the differences of the output image quality when thenumber of reference images are changed. 3 keywords, ”sunset,””spring,” and ”night sky” were chosen as the style keyword input.
We then let the number of reference images to be used for constructing the style space, to 1, 5, 10, 30, and 50 images. We performed
our algorithm with each of these numbers of reference images andobserved the output results. Note that, only one reference image,our method is equivalent with the style transfer algorithm by Gatys,
et al. [2016].

Yuri Odagiri2‡

Figure 2: Color correspondence evaluation results.

evaluate the color correspondence of the output image using purity[Weyand et al. 2012], a evaluation factor of clustering results. Let
p be the purity of Cout when Cin is taken as the ground truth ofthe clustering results, where pout ∈ [0, 1]. In images such thatp = 1, every pixel that belong to the same color cluster in theoriginal image, also belong to the same color cluster in the output
image as well. Therefore, p could be used as an evaluation index ofcolor correspondence preservation, where larger p indicates a better
preservation of color correspondence.
To compare our method with [Gatys et al. 2016], we calculated
p for (1) our result using “watercolor” as the style query, and (2)using each of the texture images in the “watercolor” texture image
set with [Gatys et al. 2016].

Figure 2 shows the results of the experiment. The results of 10 reference images has a largely different color from the original image.
The color discrepancy from the original image becomes smaller as
the number of images increase. Still, we are able to observe thedifferent styles of each of the authors.

Figure 1 shows the results of the evaluation. The blue plot shows
the distribution of p for each input texture image by Gatys et al.
[2016]. The red plot shows the result of our method. The evaluation results indicate that the proposed method preserves the color
correspondence of the image better than Gatys et al. [2016].

23

Quantitative Evaluation of Color Correspondence

For quantitative evaluation of the color correspondence of the output image, we constructed an evaluation factor of a style transfer
result based on pixel clustering as follows.
First, for the input content image Iin and the transferred output
image Iout , we perform pixelwise clustering in L∗ a∗ b∗ color space
using the k-means method. The number of clusters were set to 20.
Let Cin , Cout be the clustering results of the content image and thetransferred image, respectively. Using these clustering results, we∗ e-mail:hikaru ikuta@ipc.i.u-tokyo.ac.jp† e-mail:keisuke ogaki@dwango.co.jp‡ e-mail:yuri odagiri@dwango.co.jp

Implementation Details

Our algorithm mainly consists of the texture estimation part, whichis the novelty of this paper, and the texture transfer part, whichperforms the style transfer. The entire algorithm was mainly implemented using Chainer, a Python framework for machine learning[Tokui et al. 2015]. For the texture transfer part, we use the Adam
optimizer [Kingma and Ba 2015] to find the optimal image. For
texture feature extraction, we use a modified version of the VGG
model [Simonyan and Zisserman 2015], where the padding fromevery convolution layer is removed. This modification helps removing the artifacts in the resulting images. The number of iterations
for the texture transfer process was set to 1000 for every image inthe main paper and the supplementary material. The computation
time for image generation was about 6 minutes, on a single machineusing a GTX Titan X GPU.

4

Further Comparison with Previous Studies

Here we show further comparisons of our algorithm and Gatys, etal. [Gatys et al. 2016]. Figure 3 compares the output images ofour algorithm with Gatys, et al. [Gatys et al. 2016]. From Figure
3 we see that the results of Gatys, et al. [Gatys et al. 2016] tends
to modify the color from the original image, while our algorithm
preserves the color of the original image.

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Input Image

Result from mixuture of all 50 reference images

Results from each reference image

Figure 3: Comparison of our algorithm with Gatys, et al. Image id is 2868870, shown as first column in Figure 2.