How do Convolutional Neural Networks
Learn Design?
Shailza Jolly∗ , Brian Kenji Iwana† , Ryohei Kuroki† , Seiichi Uchida†∗ University of Kaiserslautern, Kaiserlautern, Germany

Email: sjolly@rhrk.uni-kl.de† Department of Advanced Information Technology, Kyushu University, Fukuoka, Japan

Email: {brian, kuroki, uchida}@human.ait.kyushu-u.ac.jp

Abstract—In this paper, we aim to understand the designprinciples in book cover images which are carefully crafted byexperts. Book covers are designed in a unique way, specific togenres which convey important information to their readers. By
using Convolutional Neural Networks (CNN) to predict book genres from cover images, visual cues which distinguish genres canbe highlighted and analyzed. In order to understand these visualclues contributing towards the decision of a genre, we present theapplication of Layer-wise Relevance Propagation (LRP) on thebook cover image classification results. We use LRP to explain
the pixel-wise contributions of book cover design and highlightthe design elements contributing towards particular genres. In
addition, with the use of state-of-the-art object and text detection
methods, insights about genre-specific book cover designs are
discovered.

I. I NTRODUCTION
Visual design renders specific impressions to transmit information which enriches the product’s value. However, thesevisual designs despite of being important are not analyzed
objectively or statistically. Analyzing these visual designs
enables us to understand the contained information carried bythem.
An interesting target of visual design analysis is book coverimage design where the design of a book cover can infer the
genre. Each book cover is carefully designed by typographers
and their designs represent the book contents in an intuitive
way for better sales. This association of books to specificgenres is based on the differences in their underlying bookcover designs [1]. The slight change in book cover design canreflect changes in book genre which makes design learning achallenging task for book covers.
In order to understand the design elements used for machine
aided book cover classification, we employ Convolutional
Neural Networks (CNN) [2]. In recent years, CNNs haveachieved state-of-the-art results in isolated character recognition [3], [4] and large-scale image recognition [5], [6].
Notably, Iwana et al. [1] demonstrated that CNNs can beused for genre classification based on book cover image,although with a high level of difficulty. However, that study
was subjective and not enough explanation is given as to why
the CNN performed as it did.
To interpret the reasoning behind a CNN’s prediction weused a method called Layer-wise Relevance Propagation (LRP)[7]. LRP decomposes output function on its input variables andhighlights input pixels contributing towards the network decision. It produces a layer-wise relevance heatmap by recursively
multiplying the relevance of higher layers by the normalizedfeature maps of the target layer. The heatmaps can help us todiscover the input image elements which have an effect on theclassification result.
The main contributions of this paper are threefold. Firstly,
we classified the book cover images using one-vs-others
classification with CNNs. Secondly, the models built by the
CNNs are analyzed using LRP. With LRP, we demonstrate
design elements specifically relevant to classification of thebook cover images. We show that certain objects have a strong
relevance to particular genres. Finally, we use state-of-the-art
object detection and text detection methods, namely Single
Shot Multibox Detector (SSD) [8] and Efficient and Accurate
Scene Text Detector [9], to quantitatively enforce the results
found by LRP. This reveals the specific elements in which
CNNs classify book cover images for genre classification.
The organization is as follows. Section II provides related
works in design understanding and genre classification as
well as feature visualization of CNNs. Section III reviews
the data and tools used for understanding book cover design.
Section IV presents analysis of CNN’s understanding of bookcover design. In Section V, we demonstrate the use of LRP
combined with SSD and EAST for quantitative analysis.
Finally, Section VI draws a conclusion.
II. R ELATED W ORK
A. Genre Classification
Artistic style understanding and subjective genre classification is a budding field in machine learning. For example, recent
attempts have been done to identify artistic styles and qualityof paintings and photographs [10], [11] with neural networkmodels. In addition, there have been trials to classify musicby genre [12], [13], book covers by genre [1], movie posters
by genre [14], paintings by genre [15], and text by genre [16],[17]. Also, in a general sense, document classification can beconsidered genre classification and deep CNNs are the stateof-the-art in the document classification domain [18]–[20].

B. Visualization inside of CNNs
There is a desire to visualize features and determine pixelwise attention and relevance within the hidden layers of CNNs.
However, this is a not a straightforward task [21]. Erhanet al. [21] proposed using gradient decent to maximize anode’s activation to visualize the employed features. Similar
work has been done for large-scale image classification [22].
Zeiler and Fergus [23] used deconvolutional neural networks tovisualize features learned by CNNs. In addition, they createdheatmaps by monitoring class changes systematic cover up of
portions of the images. Class Activation Maps (CAM) [24],
GradCAM [25], and GradCAM++ [26] reveal the parts ofimages which are most important to a class using global
average pooling (GAP).
Recently, LRP has been used in the fields of text [27] whereclassification scores were projected back to input features for
extracting relevant words for a specific prediction. The method
has also shown successes in model understanding in fields ofsentiment analysis [28], action recognition [29], and age andgender classification [30]. As far as the authors are aware, thisis the first time LRP has been used for the understanding ofgenre or design classification.

Fig. 1. CNN accuracy by genre.

reference classes, such as ”Engineering & Transportation,””Health, Fitness and Dieting,” ”History,” ”Medical Books,”and ”Reference.” Conversely, ”Children Books,” ”Romance,”and ”Test Preparation” had high accuracies. However, morethan just classification accuracy, the purpose of this paper isto understand why the CNN’s performed as such and reveal
the relevant parts of the images.
C. Layer-wise Relevance Propagation

III. DATA AND TOOLS FOR UNDERSTANDING BOOK COVER
DESIGN

A. Amazon Book Cover Dataset
We used the Book Cover Image to Genre dataset1 Task 1.A.
The dataset consists of 57,000 book cover images divided
into 30 classes of equal sizes. In the experiments, we used
the predefined training set and test set modified for one-vsothers classification. In this way, genre-wise training sets wereprepared with an equal distribution of positive and negative
data samples.
B. Convolution Neural Networks
CNNs are able to tackle image recognition by implementing
convolutions of learned filter-like shared weights which maintain the structural qualities of images while acting as feature
extractors [2]. For the experiment, we implement CNNs totackle book genre classification. To use the book cover images
with a CNN, they were preprocessed by scaling them to
112×112 pixels by 3 color channel images and by normalizing
the values to be between -1 and 1. The CNN used for theexperiments has six convolutional layers with Rectified Linear
Units (ReLU) activations and a softmax output layer. The
convolutional layers consisted of three layers of 10 nodes with
5 × 5 convolutional filters, one layer of 25 nodes with a 4 × 4
filter, one layer of 50 nodes with a 3 × 3 filter, and one layerof 100 nodes with a 1×1 filter. A 2×2 maxpooling layer withstride 2 was used between each convolution layer. Finally, theCNNs were trained using gradient decent with a batch size of
25 at a learning rate of 0.001 for 50,000 iterations.
The accuracy results for each genre is summarized inFig. 1. In particular, the CNNs had difficulties with the
1 https://github.com/uchidalab/book-dataset

The LRP algorithm and the LRP toolbox [31] aims to
explain the reasoning behind the decision made by a networkmodel which allows its users to validate classifier results.
LRP is mainly derived from Deep Taylor Decomposition [32],a method of decomposing network’s output predictions onto
its input variable. The results after such a decomposition isvisualized in the form of a heatmap highlighting each pixel’s
importance for the prediction.
LRP explains output function, i.e. classifier’s decision,
which helps us to derive all of the crucial pixels for aparticular prediction. In Fig. 2, the technique is shown inwhich the output value given by the network is decomposed
backwards layer by layer until it reaches the input. This
backward decomposition of network’s prediction uses local
redistribution rules for assigning relevance values Ri to eachneuron contributing towards the output, namely
X
X
X
Ri =
Rj = · · · =Rk = f (x),
(1)
i

j

k

where f (x) is the prediction function, Ri is the relevance ofnode i in the target layer, Rj is the relevance of node j of
the previous layer, and Rk is the relevance of node k of thehighest layer. The total amount of relevance is conserved inthis equation.
For the experiment, we used the α − β decomposition
formula defined byX  (ai wij )+(ai wij )−P+β
Rj , (2)
Ri =
αP
+
−
i (ai wij )i (ai wij )jwhere α and β are hyperparameters to weight the positive(ai wij )(ai wij )values of P (aand the negative values of P (a,i wij )
i wij )
i
i
respectively. Furthermore, wij is the weight between nodes i

A. Sports & Outdoors

Fig. 2. Feed forward neural network with the (left) forward pass and the(right) backward relevance calculation. The function f (x) is the prediction
outcome given input x. The variables ai and aj are the inputs for node i andj, respectively. Ri is the relevance of node i and Rj is the relevance of nodej.

Under this genre, many book covers with pictures of players
playing indoor and outdoor games were seen. Figure 3 (a)shows LRP results on these covers, which presents significanceof player’s picture on the cover. The first image in Fig. 3 (a)supports this fact with LRP being centered on players who areeither playing a sport or showing some player like gesture,with car in background adding no contribution. The second
image in Fig. 3 (a) emphasizes the animal’s importance forthis genre’s prediction.
B. Engineering & Transportationand j and ai is the input to node i. This decomposition allows
for the separation of the positive connections and the negative
connections. Values inside positive bracket indicates propagation of activating input messages while negative weight
connections indicate deactivating input values.
D. Single-Shot Multibox Detector
To develop a better understanding of the objects within bookcover images, we employed SSD [8], a state-of-the-art deepneural network based object detection method. SSD is a feed
forward CNN which produces a multi-scale collection of fixed
size bounding boxes and scores for object detection within theboxes. A final non-maximal suppression step determines the
final detections. The result of SSD is bounding box regionswith object classification labels. Using SSD, it is possible toaccurately detect multiple objects of different classes within
images.
E. Efficient and Accurate Scene Text Detector
For humans, text is an important component of book covers;
it is where the title, authors, and additional information isconveyed. However, a CNN may place a different importanceon text than humans. Thus, to analyze the relevance of text inbook covers, we use EAST [9] as a text detector. EAST uses
a multi-channel Fully Convolutional Network (FCN) and nonmaximal suppression on predicted geometric shapes to detect
multi-orient text-line and word boxes.
IV. H OW CNN S U NDERSTAND B OOK C OVER D ESIGN :Q UALITATIVE A NALYSIS
In this section, we have presented LRP results from main
genres. The analysis helped us to deduce book cover design
elements contributing towards a prediction by CNN. We used
α − β decomposition formula with values of α = 2 and β =−1 which is suggested for networks using ReLU activation
functions because it emphasizes the positive elements and deemphasizes the negative ones [7]. This is important due tothe ReLU activation function setting negative values to zero.
In the heatmaps generated by LRP under this decomposition,
pixels adding positive contribution are represented in red color
and the ones adding negative contribution are represented byblue color.

For this genre, almost all the covers with vehicle pictureson their covers were classified correctly by the network. WithLRP in Fig. 3 (b), part of image containing cars or motorbikes
seem to add more relevance than others. The last image in theFig. 3 (b) presents the cases when contribution of person image
was dominated by vehicle in the image.
C. RomanceIts obvious from the genre name that pictures of couples on
the cover are going to have more relevance and LRP results
showed this fact to be true. However, among pictures presented
in Fig. 3 (c), LRP depicted girls to add more relevancethan men or other things. The reason could reside in their
physical appearance, hairs, and choice of dresses. The same
was demonstrated in last picture of Fig. 3 (c) in which girl’shair are seen to add more relevance with zero relevance comingfrom animal part on book cover.
D. Children’s Books
Almost all the children book covers contain pictures of cartoon characters. LRP on covers from this genre showed thesecartoon characters to have higher relevance. An interesting
result is shown in first picture of Fig. 3 (d), where person isdepicted as an adversarial identity and importance of cartoonsin cover is highlighted. Some covers showed more relevancefor one object in the set of objects. Like, in Fig. 3 (d) somecartoons in last picture have higher relevance. It can be becauseof the object placement and their orientations. With the helpof this information, one can make smart choices for different
characters, cartoons and color patterns.
E. Cookbooks, Food & Wine Books
Book covers in this genre most commonly contained pictures of different kinds of food. The results in Fig. 3 (e)showed these food pictures as containment of higher relevancefor this genre. However, carefully analyzing LRP results we
discovered shapes of dishes like bowls or spoons adding
significant relevance for the genre’s prediction. So, this marks
significance of dish shape designs on covers from this genre.

(a) “Sports & Outdoors”(b) “Engineering & Transportation”(c) “Romance”(d) “Children’s Books”(e) “Cookbooks, Food & Wine”(f) “Test Preparation”

Fig. 3. Correctly recognized book covers. Object classes by SSD and text by EAST are highlighted.

F. Test Preparation
The genre contained covers with both text and pictorial
information as shown in Fig. 3 (f). With most contribution
coming from big text content on covers. Images of Fig. 3 (f)presents big texts to add more relevance than images of people.
In first image of Fig. 3 (f), despite of big girl face, relevanceis concentrated on text area of book cover.
Such analysis helped us to find design elements specificto the presented genres. To get more familiar with design,
we also presented some cases where the network was notable to correctly classify the genre. Figure 4 shows some ofthese misclassifications, mainly from the presented genres. The
correct genre names are written below the image. From theanalysis presented above, one can easily decode the reason
behind their misclassification because the designs on thesebook covers are not aligned with their genres which makes it
obvious for network to mis-classify. Here, cover from ”Sports& Outdoors” contains birds, ”Romance” cover contains text,”Cookbooks, Food & Wine Books” contain no food pictureand ”Test Preparation” cover is also without any significant
feature. LRP justifies all these covers misclassification byhighlighting these mentioned objects contributing towards the”other” class in one-vs-others.

V. H OW CNN S U NDERSTAND B OOK C OVER D ESIGN :Q UANTITATIVE A NALYSIS
A. Experiment Setup
In order to quantitatively analyze LRP, we propose using
SSD to detect objects and EAST to detect text within the bookcover images. We then use LRP to compare the relevanceof objects bound by the detection methods. The SSD wastrained on the 2012 PASCAL Visual Object Classes (VOC)
Challenge dataset [33]. The VOC dataset contains 20 classes,
including ”person,” six animal classes, eight vehicle classes,
and seven indoor object classes. While SSD trained with VOC
is intended for natural scene images, it can be used with bookcover images because book covers often contain many of theshared classes, such as ”person” and ”car.” Similarly, EAST
was trained on the 2015 ICDAR Robust Reading Competition
dataset [34] meant for scene text detection. Despite being
trained for scene text, shown in Fig. 3, EAST performs
remarkably well on book covers for detecting text.
To extract object and text bounding box information, thebook covers were prepared by scaling the images to 512×512
pixels by 3 color channels. It is important to note that theimages used for SSD and EAST were larger than the images

“Sports & Outdoors”“Romance”“Cookbooks,Food & Wine”“Test Preparation”

Fig. 4. “Misclassified” book covers with correct genre names written below
each book cover.

used by the CNN used for genre classification. This is due tothe detection methods being much more effective at the higher
resolution. To accommodate this, the bounding boxes werescaled post detection and projected onto the LRP heatmaps.
The relevance of an object Robj is calculated using the sumof the relevance within the bounding box, orX
Robj =
R(n,m) ,(3)

Fig. 5. Average object-wise relevance for text detected by EAST andeach object class detected by SSD for each book genre. Only object-genre
combinations with five or more data points are shown.

(n,m)∈B

where R(n,m) is the relevance at pixel coordinates (n, m)
within bounding box B.
B. LRP with Object Detection
A macro view of the genres can be seen by viewing the
average relevance of object classes. Figure 5 illustrates theaverage object-wise relevance of each object class as detectedby SSD and EAST for each book genre using the test set
book cover images. It should be noted that detected objects
such as ”bottle” and ”tvmonitor” were overfit to certain bookcover images because many books have plain covers which
resemble bottle labels or televisions. However, this does notmean that the information is useless. For example, from Fig. 5,”bottle” is more relevant for reference and nonfiction genres
where plain covers are common.
In addition, by examining the distribution of the Robj of
specific object classes, such as ”person,” it is possible tocreate associations between genres and detected objects. For
example, the relevance of ”person” Rperson for each genre
is shown in Fig. 6. The figure demonstrates that detected”person”s within certain genres are more relevant than othergenres. For instance, the genres of ”Romance” and ”Mystery,
Thriller & Suspense” put a high average relevance in ”person.”This indicates that ”person” is important for the CNNs of thosecategories. In addition, mentioned in Section IV-F and shownin Fig. 3 (f), people are common in ”Test Preparation” butare not necessarily relevant. This is supported by Fig. 6 whichindicates that on average, ”person” has very little relevance.
Distributions for the other object classes are provided in the
supplemental material.

Fig. 6. Box plot of relevance of ”person” Rperson for each genre. The boxes
represent the first through third quartile and the mean is in red. The whiskers
mark the minimum and maximum datum.

C. LRP with Text Detection
Figure 5 also reveals that the average relevance of text is
low. The reasoning behind this phenomenon can be explained
by Fig. 7. The figure shows that the majority of the detected
text boxes have a very small relevance Rtext , but there aresome text boxes have a higher relevance. For most genres, thetitle text contains a significant amount of relevance determinedby LRP, but the small descriptive text carries very little
relevance. Figure 3 (f) in particular demonstrates this withthe large title text having a high relevance and much of thesmaller descriptive text having near zero relevance.
VI. C ONCLUSION
In this paper, we presented importance of design in bookcovers belonging to a specific genre. The application of LRP
on the book cover dataset showed genre specific book coverfeatures. The method described most relevant parts of input

Fig. 7. Box plot of relevance of ”text” Rtext for each genre. The boxes
represent the first through third quartile and the mean is in red. The whiskers
mark the minimum and maximum datum.

book cover contributing towards a genre prediction by CNN.
We also presented quantitative analysis of LRP using an object
detection method, SSD, and a text detection method, EAST.
The analysis further demonstrates that genre classification
heavily relies on specific objects for each genres.
VII. ACKNOWLEDGEMENT
This research was partially supported by MEXT-Japan(Grant No.J17H06100).
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