SPARE TIME ACTIVITY SHEETS FROM PHOTO ALBUMS

Gabriela Csurka and Marco Bressan

Xerox Research Centre Europe, 6, ch. de Maupertuis, 38240 Meylan, France

Keywords:

Non-photorealistic rendering, Semantic segmentation, Drawings, Coloring and activity page.

Abstract:

We present a technique to generate some popular activity sheets from arbitrary images, in particular user

photographs. We focus on activity sheets that are closely linked to coloring and shape completion. We ﬁrst

introduce a baseline approach based on color regions that works well for cartoon-like images and uncluttered

photographs. In more complex scenes, we show how this approach can be integrated with global textural cues

for increasing the level of details that can convey semantic information. A ﬁnal local stage takes advantage

of object recognition and scene classiﬁcation techniques for selective detailing in the foreground background

regions. Though the resulting approach can be deployed in a fully automatic fashion, interactivity can be a

desirable feature since it allows to account for errors and, more important, increase the level of personalization.

We propose three levels of interactivity, depending on the user skills. For all steps of our system and addressed

activity sheets we show representative results.

1 INTRODUCTION

Children enjoy coloring. In addition, children like

browsing their own family albums and looking at their

own photos. If given the option, children will prefer to

select the pictures they want to color, e.g. characters

from their favorite cartoons, images from a particu-

lar subject they ﬁnd on the internet, personal family

photos, etc.

Coloring images are generally simple black and

white silhouette or border images with well sepa-

rated regions, each corresponding to a different color.

These images can also present several differences in

style (see Figure 1) leading to different spare time

activities such as unsupervised silhouette coloring,

numbered/labeled region coloring, dot linking, etc.

They are also often used in kindergarten and elemen-

tary schools, where the labeling has to be deduced as

part of an exercise e.g. a mathematical formula.

Typically, most of these activities were available

on cartoon-like images and drawings. Transforming

a printed photograph into a drawing suitable for any

of these activities requires a complex manual process

with multiple steps. With the popularity of digital

photography, it is natural to device a digital technique

to simplify this process.

The main challenge we address in this paper is to

obtain coloring pages from the arbitrary types of im-

ages children might be interested in coloring or ﬁlling

i.e. photographs and cartoons. We propose a set of

automatic and semi-automatic tools based on state-

of-the-art image analysis and processing techniques

that allow a non-expert user to generate quickly these

spare-time activity sheets from arbitrary images, par-

ticularly user photographs. These tools can be easily

plugged-in any interactive photo-editing system, can

be added to online coloring services or can be part of

a photographic print ﬂow.

The coloring page creation can be seen as a partic-

ular case and application of photographic stylization

and abstraction. Indeed, they share many common

components, such as building edge maps and image

segmentation, even face detection for non-realistic

rendering (Brooks, 2007), and therefore these com-

ponents used or developed in the former ﬁeld can be

re-used to inspire and to improve the coloring page

creation. The former techniques are not speciﬁcally

adapted to the coloring page creation and even less

to derive the divers activity sheets. Their aim is

to obtain painting rendering of the images (DeCarlo

and Santella, 2002; du Buf et al., 2006; Olmos and

Kingdom, 2006), stained glass effect (Mould, 2003;

Brooks, 2006) or to enhance the compression rate for

visual communication efﬁciency (Winnem¨oller et al.,

2006). They generally combine the luminance edge

maps with the “abstracted colors” of the image which

mutually compensate the visual imperfection of both

of them, leading to a paintings rendering stylized ef-

156

Csurka G. and Bressan M.

SPARE TIME ACTIVITY SHEETS FROM PHOTO ALBUMS.

DOI: 10.5220/0001799601560163

In Proceedings of the Fourth International Conference on Computer Graphics Theory and Applications (VISIGRAPP 2009), page

ISBN: 978-989-8111-67-8

Figure 1: Different types of activity sheets.

fect.

We propose a method to generate coloring book

pages automatically from user photos. The only

other approach we are aware of with this objective

is the commercial product Kidware Photo Color Tool

(http://www.kidware.net). The results of the latter

system are simple weighted edge maps similar to our

texture edges (see section 3.1 and Figure 3), its main

drawback beeing that many of the regions are not

closed. As coloring images are mostly designed for

young children, simplicity is desirable. Preferred im-

ages are black and white silhouette or border images

with well separated regions, each corresponding to

a different color. Another advantage of our system

compared to it is that we address a wider range of de-

rived activities (see section 4).

The paper is organized as follows. In section 2, we

describe a simple and basic method which works well

for simple images containing well contrasted and un-

cluttered objects with relatively uniform background.

In section 3 we extend this method in order to cope

with more complex scenes. In section 4 we describe

methods to obtain diverse activity sheets. Section 5

discusses multiple levels of interactivity levels and

Section 6 concludes the paper.

2 THE BASIC SYSTEM

We ﬁrst propose a relatively simple system to ob-

tain automatically coloring pages from a natural or

cartoon-like image which nevertheless goes beyond a

simple weighted edge map. This approach is suitable

for images where regions are easily distinguishable

and each region shows fairly uniform colors. Figure

2 shows a few examples obtained on real images with

this basic system.

The main steps of this system are:

Figure 2: Example results for the basic approach.

1. Color Conversion

We ﬁrst transform the image from RGB space to

some chrominance-luminance space. We choose

∗

ab, as the Euclidean distance in this space has

perceptual interpretation which can be of advan-

tage for metric-based processing such as cluster-

ing.

2. Edge-Preserving Low-pass Filtering

Next, we apply an edge-preserving low-pass ﬁl-

ter (EPLP) to the different channels of the image.

This seeks to reduce image noise which can lead

to extra edges or image segments non-relevant for

further processing. Simple median ﬁltering can be

used, or some more sophisticated methods such as

edge-preserving maximum homogeneity neigh-

bour ﬁltering (Garnica et al., 2000) or anisotropic

diffusion ﬁltering (Perona and Malik, 1991).

3. Image Segmentation

The third step consists in low level image seg-

mentation or region clustering. The most common

approaches are based on Normalized Cut (Jianbo

and Jitendra, 2000) or Mean Shift (Comaniciu and

Meer, 2002). These two methods in particular

take into account the spatial closeness of the pix-

els and therefore lead to more compact segments.

In our experiments, we used Mean Shift with a

ﬂat kernel and low color and spatial bandwidths

(σ

, σ

∈ [5, 10])). The bandwidth parameter al-

lows handling the coarseness similarly in differ-

ent images without specifying the exact number

of clusters to be found in the image. In order to

ensure we do not miss any perceptually important

boundary, we intentionally use a low bandwidth

to over-segment the image.

4. Region Merging

As we intentionally over-segment the image, in

the fourth step, we do a region merging based on a

set of rules that take into account both spatial and

perceptual information. The merging criterion is

different from the measure used by the meanshift,

SPARE TIME ACTIVITY SHEETS FROM PHOTO ALBUMS

157

meaning that the region merging leads to a differ-

ent results than using higher bandwidth would do.

The rules are very simple:

(a) If the area of the region is below a given thresh-

old (T

=0.0005% of the image area), it will

be absorbed by the most similar neighbor,inde-

pendentlyof the color differencebetween them.

(b) If the area of the region is above T

but below

a second threshold T

> T

=0.05% of the

image area) the region is merged with its most

similar neighbor only if their color similarity

is below a threshold that depends on the color

variance of the image.

the region

is kept unchanged.

In both cases the color similarity is computed as

a combination of distances in chrominance and

luminance space, giving a higher weight (impor-

tance) to the distance in the chrominance space.

This merging algorithm is applied iteratively until

no modiﬁcation is made or the maximum num-

ber of iterations is achieved. Alternatively, more

complex region merging criteria could be applied,

such as minimal cost edge removal in the cor-

responding region adjacency graph (Haris et al.,

1998).

5. Edge Detection

Finally, we extract the closed edges of the ob-

tained regions and use a morphological dilation

function to get thicker region borders.

3 VISUAL ENHANCEMENT

The proposed basic system is a simple approach that

gives satisfactory results in many cases. However, as

two of the images in the second column of Figure 3

show, it can lead to less satisfactory results as scenes

become more complex. In this section, we propose

a few extensions to the basic system to improve the

quality of the coloring pages.

3.1 Adding Texture Edges

One of the main difﬁculties of obtaining an acceptable

coloring page for a complex scene images is that gen-

erally there are several objects/elements of the scene

for which the level of “interesting” details can vary a

lot. In a coloring page application, not all details will

require the same level of attentio, e.g. a human face or

the leaves or branches of a tree. An automatic system

that has no knowledge about the image content will

Figure 3: Example results for the system with addition of

texture edges. In second column the results of the basic

system B, in the third column the edge map obtained by the

DoG algorithm normalized to [0,1] and in the last column

the weighted combination of them.

handle these regions in a similar fashion. The global

parameters can be tuned to increase or decrease de-

tails, but the same criterion will be applied to all re-

gions.

In order to handle this, we propose a ﬁrst solu-

tion based on texture/luminance edges. To extract the

luminance edges we use the Difference of Gaussians

(DoG) algorithm as it approximates well the Lapla-

cian of Gaussian, known to lead to a weighted edge

map. Furthermore, the DoG is believed to mimic well

how neural processing in the retina of the eye extracts

details from images destined for transmission to the

brain. After elimination of small edges and isolated

dots in the DoG map, these luminance edges are com-

bined with the region boundaries obtained in section

2. We used a weighted combination giving a higher

weight

to the original coloring page borders in order

to let them guide the coloring.

The main role of this combination is visual en-

hancement, however in many cases it can also com-

pensate for missing bordersbetween regions that were

wrongly merged either by the low level segmentation

or by the region merging step. These cues further help

children to better understand the content if they do not

have the original model. Figure 3 shows a few exam-

ples of coloring pages with and without adding these

texture edges. The ﬁnal results are still not perfect

compared to what a human would do manually, how-

ever it seems that children can cope well with these

small imperfections (see Figure 10).

In our experiments we used max(E, DoG

) with λ =

0.4, where the original edge map E is binary and DoG was

normalized to have values between [0,1].

GRAPP 2009 - International Conference on Computer Graphics Theory and Applications

158

Figure 4: Example results for the system with addition of

skin detection and ﬁlling with original content.

Figure 5: The sheep class mask (2nd image) obtained by

CBIS was used to eliminate the background from the col-

oring page obtained by the basic system (3rd image). Fur-

thermore, the low level segmentation was replaced by the

high level masks boundary and texture edges added inside

the relevant region (4th image).

Extracting ridges and valleys (Tran and Lux,

2004) can provide an alternative or a complement to

this approach as artists frequently take advantage of

ridges and valleys in their work.

3.2 Semantic Content Analyses

It is clear that if the system has some further knowl-

edge about the semantic content of the regions then

it can automatically handle those regions accordingly.

For example, it can increase the weights or the thick-

ness of the object’s border, merge regions within the

same semantic region, add luminance edges or ridges

only to the regions of interest, etc. We can also al-

ternatively ﬁll some of the regions with the original

content as in Figure 4 based on human skin detection.

In the last few years there were many publications

and an increasing interest on semantic segmentation

of images, i.e. assigning each pixel in an image to

one of a set of predeﬁned semantic classes. This is a

supervised learning problem in contrast to the “classi-

cal” unsupervised low-level segmentation. There are

three main groups of techniques that can be integrated

with the system we propose:

1. Foreground-background Separation

The objective here is to separate some foreground

object or region of interest (ROI) from the back-

ground, not necessarily knowing what the object

is. In most approaches proposed in the litera-

ture, the algorithm requires manual initialization,

which can be simple enough to be done by a child

(e.g. drawing a box or a contour around the ROI).

In a pure automatic case, the system can either as-

sume that the ROI is in the center of the image

or use Visual Attention Maps (Itti et al., 1998)

to initialize. After initialization, to get the fore-

ground/background separation one might use im-

age matting techniques (Sun et al., 2004), active

contours (Juan et al., 2006) or to apply GrabCut

(Rother et al., 2004).

2. Object Detection and Localization

The main idea is to simultaneously recognize and

segment out a predeﬁned object class such as per-

son, car, horse, etc. The techniques that address

this problem (Winn and Jojic, 2005; Levin and

Weiss, 2006) require that the set of object classes

be predeﬁned. In general, the system has to be

trained with fairly clean examplary images. Faces

and human skin are of particular interest in color-

ing page because users are more sensible concern-

ing the results of segmentation or edges on a face

or human body than on any other object. Much

of the state of the art focuses on these categories:

see (Viola and Jones, 2001; Yang et al., 2002) for

face and (Vezhnevets et al., 2003; Tomaz et al.,

2003) for human skin detection. As above, the ini-

tial detection results can again be further reﬁned

by matting or active contours to get a better ob-

ject/background segmentation.

3. Semantic based image Segmentation

In contrast to the two previous cases, in this ap-

proach the image can be partitioned generally in

more than two semantically labeled (meaningful)

regions. Of course the previous cases can be seen

as particular instances of the semantic segmen-

tation problem where only two classes are de-

ﬁned. Several techniques were recently proposed

to solve this problem (Shotton et al., 2006; Yang

et al., 2007; Csurka and Perronnin, 2008). We

used the last one in our experiments (called CBIS

in which follows).

The integration of these techniques with the color-

ing page system can hence further enhance the visual

quality of the coloring page (see Figures 4 and 5), but

they are of particular interest for the derived activity

sheets as we will see.

SPARE TIME ACTIVITY SHEETS FROM PHOTO ALBUMS

159

G − Green

K − Black

B− Brown

Figure 6: An example of a labeled coloring page.

4 DIVERSE ACTIVITY SHEETS

4.1 Region Labeling

In contrast to the unsupervised case, the idea here is

that the child has to follow some rules to color each

region. It can be simply the recognition of some let-

ters as in the 2nd example of Figure 1), or it can be

more complex such as mathematical or logical for-

mulas. The latter are often used in kindergarten and

elementary school with pedagogical purposes.

With our system, this can be done automatically,

because (1) we have closed regions and (2) we have a

representative color of each region (mean color, clus-

ter center or mean shift mode). We can therefore

select a set of standard colors (e.g. using the well-

known, standard NBS-ISCC color name dictionary

http://www.anthus.com/Colors/NBS.html), to ﬁnd for

each region the selected standard color which is clos-

est to its representative color and plot the correspond-

ing letters, formulas, shape, etc (depending on the

children’s age) onto it. Eventually, on the border or

next to the image, the legend is printed with the labels

and the corresponding color (see Figure 6).

4.2 Link the Dots

A second popular activity sheet example is the link-

the-dots sheet (see 5th example in Figure 1). These

sheets are also often used by kindergarten as they help

children to learn number ordering and the alphabet.

The main idea is to take a single object boundary

using one of the techniques described in section 3.2,

sample dots on it, label them with letters, numbers or

formulas following the contour and eventually delete

the original contour.

The dots on the boundary can be sampled uni-

formly or obtained by more complex algorithms that

seek for corners and inﬂexions points such as chain

code detection (Liu and Srinath, 1990), local contours

(Reche et al., 2002), direct estimation of the curve

and its high curvature points (Chetverikov and Sz-

abo, 1999; Hermann and Klette, 2005). We used a

Figure 7: Example of automatically obtained follow-the-dot

examples. In the ﬁrst case we used the bird class mask ob-

tained by CBIS and the dots were obtained by CSS corner

detector on the boundary of the mask. We also added the

DoG edges inside the object region to enhance the ﬁnal re-

sult. In the second case, we initialized the GrabCut (Rother

et al., 2004) with a box centered in the middle of the image

and made forground/background separation. We show the

original object boundary on the results for pure visualiza-

tion purposes.

local implementation of the popular method, the Cur-

vature Scale Space (CSS) based corner detector (Ab-

basi et al., 1999; He and Yung, 2004) to obtain a set

of dots on the object boundary (see Figure 7).

4.3 Object Discovery through Coloring

Finally, a third type activity sheet is to discover hid-

den objects through coloring. The objective can be a

simple foreground or background coloring, labeled by

dots as in 4th example in Figure 1; or more complex

where a set of colors has to be used and the individual

labels are mathematical or logical formulas (as in 6th

example of Figure 1).

These sheets can also be derived from our

solution when we have the knowledge of fore-

ground/background or alternatively the semantic re-

gions (see section 3.2). In these cases, the idea is to

either use the over-segmentation we already have in

step 3 (section 2) or we can combine the high level

segmentations with some random partitioning of the

image. Finally, the dots/formulas can be added auto-

matically to sub-regions according to their semantic

meanings

. We could also use forground/background

In our experiments (see Figure 8) we obtained the se-

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160

Figure 8: Example results for hidden object sheet. We used

two different image partitioning strategy (many others can

be used): partitionning the image by random parralel lines

and random ellipses (third image) and respectively combin-

ing a few existent coloring pages as we have closed regions

for them (fourth image). In both cases the dots were added

to the regions that had a minimum of 70% overlap with the

estimated “horse + person” mask (second image).

detection as described in section 3.2.

5 INTERACTIVITY

Application of the described steps, using the default

parameters and having pre-selected the output style,

results in a fully automatic coloring image generator.

Alternatively, we can envisage the integration of the

system with any photo editing tool or interactive col-

oring systems by adding different interactivity levels

to the system:

1. At a ﬁrst level of interaction the system allows the

user (child) to select or to upload a photo. The

photo is automatically processed and a set of col-

oring pages (with or without texture edges) and

activity sheets are proposed.

2. A second level of interaction would be designed

for older children or parents allowing to mod-

ify/adjust some of the parameters of the system.

However, the interaction with the parameters has

to be user friendly, such as choosing between less

or more details, thiner or thicker edges, adding

or not texture edges, adding letters or formulas,

etc. Then the corresponding interior parameters

are adjusted accordingly.

3. Finally a highest level of interaction could allow

matic meanings of the regions with the CBIS, consider-

ing all Pascal VOC 2007 classes as relevant regions (see

http://pascallin.ecs.soton.ac.uk/challenges/VOC for further

details).

Figure 9: Example results for the system interactive region

ﬁlling with original content.

the user to edit the obtained coloring page by

some interactive tools such as:

• Erasing Tool: to delete selected edges (the two

regions separated by the selected edges will be

merged automatically).

• Dot Adding Tool: to complement follow-the-

dot pages

• Region Filling Tool: to ﬁll the region either by

the texture edges, the mean color value or orig-

inal content

(see Figure 9).

6 CONCLUSIONS

We propose a system that partially or totally auto-

mates the creation of a range of spare time activity

sheets from photo albums. We provide solutions for

unsupervised coloring page creation but also show

how we can derive related activity sheets. The main

originalities and advantages of the proposed system

are that the original image is arbitrary, and that the

coloring pages and derived activity sheets can be cre-

ated automatically. We also propose different levels

of interactivity that depend on the skill requirements

we wish to impose.

The approach is simple and hence can be easily

integrated in any photo editing or online coloring sys-

tem. As there is no real groundtruth neither bench-

mark data it is difﬁcult to establish the best parameter

set of the system. In our experiments we processed

hundreds of images of the Pascal VOC 2007 Chal-

lenge as we had for them the CBIS estimates and vi-

sually compared them to tune the examplary parame-

ters reported and used to obtain the images shown in

the paper. They are probably not the best choises, and,

The main goal of such interaction can be visual quality

enhancement but also can provide extra fun to the children.

Indeed, in the case of interactive coloring of the page this

can also be seen as a “magical pencil” that allows the child

to ﬁll image regions with the original content of the image

instead of coloring it.

SPARE TIME ACTIVITY SHEETS FROM PHOTO ALBUMS

161

as a future work, we intend to do intensive preference

user studies to better establish these parameters and

also to compare different alternatives of the system.

We provided gave a set of automatically created

color pages to a few children (see some of them in

Figure 10) who accepted

and enjoyed coloring them

for us.

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Figure 10: Example of coloring pages colored by Anton (11 years), Gabriel (9 year), Johanna (8 years), Elisabeth (7 years)

and Mikha¨el (5 years).

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