Fashion Coordinates Recommender Systemusing Photographs from Fashion Magazines
Tomoharu Iwata
Shinji Watanabe Hiroshi Sawada
NTT Communication Science Laboratories
2-4 Hikaridai, Seika-cho, Soraku-gun, Kyoto, Japan{iwata.tomoharu,watanabe.shinji,sawada.hiroshi}@lab.ntt.co.jp
Abstract
Fashion magazines contain a number of photographs of fashion models, and their clothing coordinates serve as useful references. In this paper, we propose a recommender system for clothing coordinates using full-body photographs fromfashion magazines. The task is that, given a photograph of a fashion item (e.g. tops) as a query,to recommend a photograph of other fashion items(e.g. bottoms) that is appropriate to the query. With
the proposed method, we use a probabilistic topic
model for learning information about coordinatesfrom visual features in each fashion item region.
We demonstrate the effectiveness of the proposed
method using real photographs from a fashion magazine and two fashion style sharing services withthe task of making top (bottom) recommendations
given bottom (top) photographs.

1

Introduction

Many people are interested in their fashion. They regularly
check trends in fashion magazines, and refer to them whenbuying clothes and choosing coordinates. Fashion magazines
contain a number of photographs of fashion models, and theirclothing coordinates serve as useful references.
In this paper, we propose a recommender system for clothing coordinates using full-body photographs from fashion
magazines. The task is that, given a photograph of a fashion item (e.g. tops) as a query, to recommend a photographof other fashion items (e.g. bottoms) that is appropriate to
the query. The proposed method can be helpful when selecting clothes to wear from one’s wardrobe. Sometimes, it isdifficult for a person to make a quick decision about stylish
coordinates. The proposed method can also be used whenpurchasing clothes that match one’s own clothes, and whenpurchasing matching clothes for the top and bottom half ofthe body in an online store. If the store employs experts toprovide advice, we can ask them about coordinates. However, online stores have no such staff.
With the proposed method, coordinate information islearned from visual features in each fashion item region (e.g.
top or bottom half of the body) using full-body photographsfrom fashion magazines. We use a topic model to learn coordinate information that includes the intrinsic relationshipbetween fashion items. The proposed topic model is a probabilistic generative model of images for each fashion item region. The learned coordinate information is used to recommend coordinates. Intuitively, the proposed method finds reference photographs that are similar to the query photograph,and recommends fashion items that are similar to those in thefound reference photograph. By using photographs that are
taken from the user’s favorite fashion magazines to train the
topic model, the proposed method can recommend personalized coordinates that coincide with the user’s preference.
The proposed method is novel in the sense that it learns
information about coordinates automatically from a collection of reference photographs. Some fashion recommendation methods have been proposed [Nagao et al., 2008;
Shen et al., 2007; Yonezawa and Nakatani, 2009]. For example, [Shen et al., 2007] proposed a recommender system for
clothing to suit a given situation using item attributes such asbrands, types (e.g. jeans) and styles (e.g. formal, trendy andsporty). [Nagao et al., 2008] recommends clothing relevantto the weather and the situation using annotated data. Thesemethods require system developers and users to attach metadata to clothes, which is a tiresome and time-consuming task.
On the other hand, the proposed method does not require theattachment of meta-data; it only requires users to take photographs of their own clothes and system developers to collect reference photographs. We can easily collect reference
photographs from fashion magazines, online clothes stores,
or fashion photograph sharing services, such as Chicisimo1
and Weardrobe2 .
The remainder of this paper is organized as follows. In Section 2, we present a method for detecting the top and bottom
regions in full-body photographs from a wide variety of photographs. In Section 3, we propose a topic model for learning
information about coordinates from the reference full-bodyphotographs. In Section 4, we describe a coordinates recommendation method that uses the topic model. In Section 5, wedemonstrate the effectiveness of the proposed method by using real photographs from a fashion magazine and two style
sharing services with the task of top (bottom) recommenda1
2

http://chicisimo.com
http://www.weardrobe.com

Symbol
D
R
r
V
(r)
Nd
(r)
xdn
Figure 1: Example of top and bottom region detection usinga face detection algorithm.

K
(r)
zdn
θd

tions given bottom (top) photographs. Finally, we present
concluding remarks and a discussion of future work in Section 6.

2

(r)

φk

Top and Bottom Region Detection

The proposed method described in Sections 3 and 4 can beused to recommend arbitrary fashion items, such as tops, bottoms, hats, bags, shoes and accessories. However, this paper focuses on top (bottom) recommendations given a bottom(top) photograph, since it is the most basic and frequently
occurring situation. In such cases, the proposed method requires a collection of pairs of top and bottom images to learn
coordinates. This section describes a simple method of identifying full-body photographs from a collection of photographs
in fashion magazines, and detecting their top and bottom regions.
In addition to full-body photographs of fashion models,
fashion magazines include photographs of clothes, bags, accessories, wording and logos. Therefore, we need to select
only full-body photographs from all the given photographs.
We utilize a face detection algorithm [Rowley et al., 1998;
Viola and Jones, 2004] for this task. First, we detect the location of faces in each photograph. Next, we select full-bodyphotographs by using the following two rules: they must contain a face region, and they must have sufficient space for a
full body; the space can be calculated from the size and position of the face region. When more than one face region isdetected, we use one that is the closest to the vertical centerline of the photograph.
We determine the top and bottom regions by assuming thatthe top region is double the width and 2.5 times the heightof the face region, and the bottom region is double the widthand 3.5 times the height of the face region. Figure 1 shows anexample of top and bottom region detection.
We can detect the top and bottom regions directly by using
pose estimation instead of face detection algorithms. Somealgorithms for detecting human body parts have been proposed [Ramanan, 2007; Andriluka et al., 2009]. However, we
utilize a face detection algorithm for simplicity, because facedetection performance is superior to that for other regions,and most of the poses in fashion magazine photographs show
the front view of a standing model.

3

Table 1: Notation
Description
set of reference full-body photographs
set of regions, e.g. R = {top, bottom}
region, r ∈ Rnumber of unique visual features
number of features in region r of photograph d
nth feature in region r of photograph d,(r)
xdn ∈ {1, · · · , V }number of latent topics
latent topic of the nth feature in region r(r)of photograph d, zdn ∈ {1, · · · , K}
topic proportions for photographd,∑θd = {θdk }K
k=1 , θdk ≥ 0,k θdk = 1
multinomial distribution over features
for topic k in region r,
∑ (r)
(r)
(r)
(r)
φk = {φkv }Vv=1 , φkv ≥ 0, v φkv = 1

Topic Model for Coordinates

Let D be a set of reference photographs. Each reference photograph consists of a pair of visual features, one in the top
(t)
(b)
(r)
and one in the bottom region (xd , xd ). Here, xd represents visual features in region r of photograph d. The top andbottom regions can be detected using the method described in
Section 2. We can use arbitrary visual features, such as color,
texture and local descriptors such as the Scale Invariant Fea(r)ture Transform (SIFT) [Lowe, 2004]. We assume that xd
(r)

(r) N

(r)

d
is represented by bag-of-features; i.e. xd = {xdn }n=1,(r)where xdn is the nth visual feature in region r of photographd. Our notation is summarized in Table 1.
We use a topic model for learning information about coordinates since the topic model can extract the intrinsic relationship between fashion items from full-body photographs.
A topic model is a probabilistic generative model for discrete data such as text documents. Topics in fashion photographs, for example, represent fashion categories, suchas casual, formal, elegant, classic and conservative. Topic
models are successfully used for a wide variety of applications including information retrieval [Blei et al., 2003;
Hofmann, 1999], collaborative filtering [Hofmann, 2003;
Iwata et al., 2009a], and modeling images [Blei and Jordan, 2003; Cao and Fei-Fei, 2007; Iwata et al., 2009b]. The
proposed model is an extension of latent Dirichlet allocation(LDA) [Blei et al., 2003], which is a representative topic
model.
The proposed topic model can learn cooccurrence information about visual features in top and bottom regions. For example, a white top is likely to be coordinated with a dark blue
bottom when color information is used for the visual features.
Using the learned cooccurrence information, we recommend
coordinates as described in the next section. The proposedmodel assumes that photograph d has its own topic proportions θd , which is a low dimensional intrinsic representationof the photograph. Each topic has its own visual feature prob-

z
α

(t)

(t)

x

N(t)

θ
z (b)

φ (t)

β

φ(b)

β(b)

x(b)
(b)

N

D

distributions. The first factor on the right hand side of (1) is
calculated as follows:)|D| ∏ ∏(Γ(Kα)
k Γ(Ndk + α)
, (2)P (Z|α) =Γ(α)KΓ(Nd + Kα)

(t)

d

K

Figure 2: Graphical model representation of the proposed
topic model for recommending top and bottom coordinates.
(r)ability depending on region φk , which represents cooccur(r)rence information. The visual feature xdn is generated ac(r)cording to the region dependent probabilities φ (r) after topic
zdn

(r)

zdn is chosen from topic proportions θd .
In summary, the proposed model assumes the following
generative process for a set of reference photographs X =(r){xd }r,d ,1. For each topic k = 1, · · · , K:(a) For each region r ∈ R:
i. Draw the visual feature probability for region r(r)
φk ∼ Dirichlet(β (r) )where Γ(·) is the gamma function, |D| is the size of set D,
Ndk is the number of∑features assigned to topic k in the photograph d and Nd = k Ndk . Similarly, the second factor isgiven as follows:∏ ( Γ(V β (r) ) )K ∏ ∏ Γ(N (r) + β (r) )w
kv,
P (X|Z, β) =(r) )V
(r)
(r) )
Γ(β
Γ(N
r
k
k +Vβ
(3)
(r)
where Nkv is the number of times feature v has been as∑(r)(r)
signed to topic k in region r, and Nk = v Nkv .
The inference of the latent topics Z given reference photographs can be efficiently computed using collapsed Gibbs
sampling [Griffiths and Steyvers, 2004]. Given the current
state of all but one variable zj , where j = (d, r, n), the assignment of a latent topic to the nth feature in region r ofphotograph d is sampled from the following probability:(r)

P (zj = k|D, Z\j ) ∝ (Ndk\j + α) ·2. For each reference photograph d ∈ D:(a) Draw topic proportions θd ∼ Dirichlet(α)(b) For each region r ∈ R:(r)i. For each feature in region r, n = 1, · · · , Nd :(r)

A. Draw topic zdn ∼ Multinomial(θd )
(r)

(r)

B. Draw feature xdn ∼ Multinomial(φ (r) )
zdn

(r)

where α and β are Dirichlet distribution parameters. Figure 2 shows a graphical model representation of the proposed
topic model for top and bottom regions, where shaded and unshaded nodes indicate observed and latent variables, respectively. The structure of the model is the same as that of thepolylingual topic model [Mimno et al., 2009], which is used
for extracting topics from documents in many languages.
The proposed topic model can learn coordinate information about other regions in addition to top and bottom regions, such as hats, bags, shoes and accessories. In thiscase, a reference photograph consists of a set of visual fea(r)tures with multiple regions {xd }r∈R , where e.g. R ={top, bottom, hat, bag}.
The joint distribution of visual features and latent topics is
described as follows:
P (X, Z, |α, β) = P (Z|α)P (X|Z, β),
(r)

(1)
(r)

where Z = {zd }r,d is a set of latent topics, zd
(r)
Nd

(r)
{zdn }n=1 , and β== {β }r is a set of Dirichlet parameters. We can integrate out multinomial distribution parame(r)ters, {θd }d and {φk }k,r , because we use Dirichlet distributions for their priors, which are conjugate to multinomial(r)Nkxj \j + β (r)(r)

Nk\j + V β (r),(4)

where \j represents the count when excluding the jth feature.
The parameters α and β (r) can be estimated by maximizing the joint distribution (1) by using the fixed-point iteration
method described in [Minka, 2000] as follows:∑ ∑Ψ(Nkd + α) − |D|KΨ(α)α ← α d∑ k,
(5)
K ( d Ψ(Nd + Kα) − |D|Ψ(Kα))
∑ ∑
(r)
(r)
) − KV Ψ(β (r) )kv Ψ(Nkv + β),
β (r) ← β (r) (∑(r)(r) ) − KΨ(V β (r) )
Ψ(N
+
V
β
V
k
k
(6)
where Ψ(·) is the digamma function defined by Ψ(x) =∂ log Γ(x). By iterating Gibbs sampling with (4) and maxi∂x
mum likelihood estimation with (5) and (6), we can infer latent topics while optimizing the parameters.
(r)
We can estimate φkv by using the following equation:
(r)

(r)

φ̂kv =

Nkv + β (r)(r)

Nk + V β (r),(7)

which is used for recommending coordinates as described in
the next section.

4

Coordinate Recommendation based on
Topic ModelIn this section, we describe a method for recommending coordinates using the topic model learned with reference photographs. Intuitively speaking, we recommend a bottom (top)that has the closest topic proportions to those of the given top(bottom).

Let C (t) and C (b) respectively be a set of photographsof top and bottom garments that the user owns. The proposed recommendation method finds a bottom (top) photograph ĉ ∈ C (b) (ĉ ∈ C (t) ) that matches the given top (bottom) photograph c∗ ∈ C (t) (c∗ ∈ C (b) ). Each photograph(r)
is associated with visual features xc , c ∈ C (r) . Note that(r)photographs in C
need not be paired; they can be photographs of a shirt, a blouse or a skirt. We can create thosedata from full-body photographs of the user by using the region detection method described in Section 2. Here, we aim
to recommend coordinates from among the clothes that theuser owns. With recommendation in an online store, C (r)can be the store’s photographs.
In the following, we explain the case of a bottom recommendation given a top. We can recommend a top given abottom in the same way. First, we estimate topic proportions
θc∗ for a given top photograph and the user’s own bottom
photographs C (b) using the topic model learned with refer(r)ence photographs Φ̂ = {φ̂k }k,r . The assignment of a latent
topic zj , where j = (c, r, n), is sampled as follows:(r)

P (zj = k|x(r)c , zc\j , Φ̂) ∝ (Nkc\j + α) · φ̂kxj ,

(8)

N (r)

(r)

c
where zc = {zcn }n=1
represents a set of latent topics inregion r of photograph c. Then, we recommend the bottom ĉthat has the closest topic proportions to those of the given top
photograph as follows:ĉ = arg max S(θc∗ , θc ),c∈C (b)(9)where S(·, ·) represents a similarity function. For the similarity, we use the following negative KL divergence:
∑
θc k
(10)
θc∗ k log ∗ ,S(θc∗ , θc ) = −θckksince topic proportions θc are a probability distribution, andthe KL divergence is one of the most commonly used distances for probability distributions. Although the recommendation of one photograph is assumed above, we can also recommend n photographs with the highest S(θc∗ , θc ) values.
Figure 3 shows the framework of the proposed coordinate
recommendation method. In the framework, full-body photographs for reference are converted to visual features foreach region, and the proposed topic model is learned using
the visual features. The query top and the user’s own bottom
photographs are also converted to visual features, and topic
proportions of the query and the user’s own photographs are
estimated using the learned topic model. Then, a bottom isrecommended that has the closest topic proportions to thoseof the query top.

5

Experiments

5.1 Data Set
We evaluated our proposed coordinate recommender system
based on the topic model using three real photograph collections: Magazine, Chicisimo and Weardrobe. The Magazine data consists of photographs taken from 32 copies of afull-bodyphotos

topic
proportions

visual features
for each region

topvisualfeaturesquery topphotobottomtopic
model

own bottom
photosrecommended
bottomfind a bottom that has therecommend closest topic proportions tosystemthe query top

Figure 3: Framework of proposed coordinate recommendation method.
women’s fashion magazine. There were 14,813 photographs
including photographs of clothes, bags, shoes, cosmetics
and accessories as well as full-body fashion model pictures.
Chicisimo and Weardrobe are fashion community web services for women in which users can share style photographs.
The communities have a rule stating that shared photographs
should show the full-body of the user. We used 1,059 and
1,410 photographs from Chicisimo and Weardrobe data, respectively.

5.2 Region Detection
In the Magazine data, 2,062 photographs out of 14,813 were
estimated as full-body by the proposed method describedin Section 2. We checked the results for top and bottom region extraction from the 2,062 photographs by human judgement. The regions were adequately extracted
in 1,502 photographs (73%). We also checked the Chicisimo and Weardrobe data, and the regions were adequately
extracted from 75%(797/1,059) and 71%(1,004/1,410) of
the photographs, respectively. For the Magazine data, wechecked randomly sampled 221 photographs that were determined as not being full-body using the proposed method.
81%(180/221) of the photographs were truly not full-body.
Although the proposed detection method is simple, it can detect top and bottom regions with the high true positive rate,
and the high true negative rate. In the following recommendation experiments, we used 1,502, 797 and 1,004 photographs
respectively from the Magazine, Chicisimo and Weardrobe
data that were checked by human judgement.

5.3 Baseline Recommendation Method based on
Visual Feature Similarities
Before describing our experimental coordinate recommendation results, we describe a baseline method for recommendation based on visual feature similarities and compare it withthe proposed topic model based method.
Assume that a photograph of a top c∗ is given as a query.
We would like to find a photograph of a bottom that matches
a given top from a set of the user’s own bottom photographs

C (b) . First, the baseline method finds the top photograph dˆthat is closest to the given top c∗ from a collection of reference photographs D as follows:(t)dˆ = arg max F (x(t)
c∗ , xd ),
d∈D

(11)

where F (·, ·) represents a similarity function, For the similarity function, we used the cosine similarity in the experiment,
which is a commonly used similarity function in the bag-offeatures representation. Then, the baseline method recommends bottom photograph ĉ that is the most similar to thebottom in the found reference photograph dˆ from the user’s
own bottom photographs C (b) :(b)ĉ = arg max F (xdˆ , x(b)c ).
c∈C (b)(12)

In the same way, the baseline method can recommend amatching top given a bottom.

5.4 Recommendation
We evaluated the proposed method for recommending coordinates based on a topic model with a baseline method basedon the similarities between visual features.
We used a color histogram for the visual features as described in [Swain and Ballard, 1991]. Specifically, the threecolor axes used for the histogram are defined as follows:
rg = r − g, by = 2b − r − g, wb = r + g + b, where r,
g and b represent red, green and blue signals, respectively.
The wb axis, which indicates intensity, was divided into eightsections, and the rg and by axes were divided into 16 sections, and therefore the dimension of the visual features was
V = 2, 048.
We generated 100 sets of training and test data, in which 10% of the samples were randomly selected as test data. With
the proposed method, the training data set is assumed to be acollection of reference photographs D, and the parameters of
the topic model are inferred using the training data. The test
data are assumed to be sets of photographs C (t) and C (b)owned by a user. In the evaluation, each of the top (bottom) photographs in the test data is given as the input, anda matching bottom (top) photograph is recommended, while
the bottom (top) photographs are held out. We assumed thatthe correct answer for a recommendation given a top (bottom)
photograph was the bottom (top) photograph that was pairedwith the given photograph.
For the evaluation, we used the following two measurements: n-best accuracy and similarity. The n-best accuracy
represents the rate of recommending the correct bottom (top)photograph with n recommendations given a test top (bottom)
photograph. The similarity evaluation measurement is the average similarity between the recommended clothing and theheld-out paired clothing.
Figure 4 shows the average accuracies and the standard deviations over 100 experiments on the three data sets. The Proposed plot represents results for the proposed method where
the number of topics K = 20, the Baseline plot represents
results for the baseline method based on the similarities between visual features, and the Random plot represents results for a method that recommends clothes randomly. Fig-ure 5 shows the average similarities and the standard deviations with different numbers of topics. The proposed method
achieved higher accuracies and similarities for all three data
sets compared with the baseline and random methods. This
result indicates that the proposed method can learn information about coordinates adequately using the topic model.
Since the proposed method uses the similarities of extracted
low dimensional intrinsic representations, it is more robust
than the baseline method, which naively uses similarities between high dimensional visual features.
When we analyze the extracted topics, photographs withsimilar coordinates form clusters. For example, a topic isfrequently assigned to photographs showing white one-piecesuits, and another topic is frequently assigned to photographs
showing white t-shirts and blue jeans, which correspond toformal and casual topics, respectively.
The computational time of a query response was 0.04 second, which includes estimating topic proportions of the queryand sorting photographs by their similarities.

6

Conclusion

We have proposed a topic model for recommending fashion
coordinates using a collection of photographs from fashion
magazines. We have confirmed experimentally that the proposed method can learn information about coordinates appropriately, and can be used for recommending fashion coordinates.
Although our results have been encouraging to date, ourapproach can be further improved in a number of ways. First,
we would like to improve the region detection method. We
utilized a face detection algorithm for simplicity to detect
top and bottom regions. However, top and bottom regions
may not appear immediately below the face region when the
fashion model leans, sits or lies down; the border of top andbottom regions differs depending on the clothes, the fashion model’s style, and the angle at which the photographs
were taken. By detecting regions directly using pose estimation algorithms, the method becomes applicable to photographs with a wide variety of poses. Second, the proposed
topic model can be used to recommend coordinates for fashion items other than tops and bottoms, such as shoes, bags,
hats and accessories. Third, we would like to investigate appropriate visual features for recommending coordinates. In
our experiments, we used color information for the features.
However, the texture and the shape of items may also constitute important information. Finally, it is valuable to recommend coordinates that match the user’s preference and situation, such as business, dating or formal. This personalizedrecommendation can be achieved by using the history of theclothes the user has worn and the context.

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0.8

0.9
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accuracy

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#topics(b) Chicisimo

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