DEIM Forum 2017 G6-1

Learning Subjective Adjectives from Images by Stacked Convolutional
Auto-Encoders
Bei LIU† , Makoto P. KATO† , and Katsumi TANAKA†† Department of Social Informatics, Kyoto University
Yoshida-honmach, Sakyo-ku, Kyoto
606–8501 Japan
E-mail: †{liubei,kato,tanaka}@dl.kuis.kyoto-u.ac.jp
Abstract In this research, we tackle a problem of searching images based on “subjective adjective noun pair”
queries and ranking images considering both “noun” and “subjective adjective”. Conventional researches that focus on analyzing “subjective adjective” (e.g. sentiment) and “noun” (e.g. object) from images rely on supervised
learning method which requires a large number of training data and the reliability of data will influence learning
performance a lot. Moreover, in the context of image search, it is unrealistic to include all possible “subjective
adjective noun pair” queries and give labels to their images. For this reason, we propose to find truly relevant
images of “subjective adjective noun pair” queries by learning discriminative features from pair-wise diﬀerence between images with unsupervised deep learning method (pair-wise stacked convolutional auto-encoders) and thenrank images based on their relevance to “subjective adjective” and “noun”. We conduct experiments with flickr
images to show the eﬀectiveness of our approach.
Key words image search, unsupervised, deep learning

1. Introduction
Subjective adjectives refer to adjectives that express opinions and evaluations in natural language [16].

Recently,

analysis of “subjective adjective” (e.g. sentiment) from visual contents has attracted considerable attentions [3] [4] [17].
Compared with object detection, scene categorization, textual analysis, or pure visual attribute analysis, subjective
adjective analysis is more subjective and holistic, and it is(a) relevant image of “happy (b) relevant image of “happy girl”（注 2）dog”（注 1）

Figure 1 Two relevant images of the same “subjective adjective”
but diﬀerent “nouns”.

related to broader and more abstract image analysis. The focus of this paper is image retrieval and in particular imagesof “subjective adjective noun pair” queries.
In contrast to ANP classification problems [3] [4] [12] which
try to classify an image into adjective noun pair concepts,
our purpose is to estimate relevance of an image to a given“subjective adjective noun pair” query. In order to achieve
this goal, we need to compute the relevance between images
and the query which is diﬃcult to define and construct a
mapping from completely visual contents to subjective adjectives and nouns. Narihira et al. [12] succeed in building avisual sentiment ontology from visual data and respects visual correlations along adjective and noun semantics with afactorized CNN model. However, their method depends onsupervised learning which requires a large number of labeledimages and the reliability of data will influence learning per-formance. In the context of image search, it is unrealistic to
include all possible “subjective adjective noun pair” queries
and give labels to their images. Intuitively, the discriminative features that are critical to relevance estimation vary on
diﬀerence nouns in the context of the same subjective adjective. For example, in Figure 1, opening mouth and hanging
tongue might a sign of “happy dog” while the rising radianof the mouth is enough to indicate a “happy” for the noun“girl”.
Given a query, current image search engines defines an image’s relevance to the query utilizing the image’s contextual
information and measure the image’s similarity with other（注 1）：https://www.flickr.com/photos/151892120@N03/32723517222（注 2）：https://www.flickr.com/photos/12287146@N04/5250858198

result images (e.g. VisualRank). VisualRank [7] gives hightures that can present discriminative diﬀerence between two

weight to images that is most similar to other images. This

image sets. These discriminative features are then used toapproach might be enough for searching images of objects,

estimate images’ relevance to the “subjective adjective noun”

since features of objects are much easier to be captured by ex-

query.

isting feature extracting algorithm. However, with the query

Two contributions of this paper are briefly described:

including “subjective adjective”, the problem is not that sim-

（ 1 ） We proposed to learn truly relevant training dataset

ple. Firstly, many images are not the exact reflection of theirof “subjective adjective noun” query by comparing images ofsurrounding texts that include “subjective adjective” andto pseudo-relevant image set and pseudo-irrelevant image set,“noun”, and this results in many irrelevant images, especially（ 2 ） We proposed to find discriminative features to rep-

irrelevant to the “subjective adjective” query. Secondly, dis-resent diﬀerences of images by unsupervised deep learning

criminative features that are responsible for the “subjective

method (stacked convolutional auto-encoders)

adjective” query is not easy to extract with existing feature
extraction methods, since the way to compute those features
diﬀers from query to query. For an instance, color is an effective feature for “spicy chicken” while “size proportion isbetter to identify “soft pancake”.

We conduct experiments with images from flickr to evaluate the eﬀectiveness of our approach.

2. Preminaries
2. 1 Auto-Encoder

Although it is not guaranteed that truly relevant images

Encoder-decoder paradigm is used in many unsupervised

account for the most among result images of “subjective ad-feature learning methods, such as Predictability Minimiza-jective noun pair” query, we assume that there are more truly

tion Layers [14], Restricted Boltzmann Machines (RBMs) [5]relevant images in the result images than in images of only

and auto-encoders [6].

“noun” query. Thus, we propose to compare two result image sets with such assumption: discriminative features that

Here we briefly specify the auto-encoder (AE) frameworkand its terminology.

help add the relevance to “subjective adjective” are similar

Encoder:a deterministic function fθ mapps an input vector

for one object (“noun”) in certain dimensions. Note that wex ∈ Rd into hidden representation y ∈ Rd : y = fθ (x) =focus on finding diﬀerences that are important for “subjec-

σ(Wx+b) with parameters θ = {W, b}, where W is a d′ ×dtive adjective” since we consider it as a more diﬃcult prob-

weight matrix and b is an oﬀset vector of dimensionality d′ .

lem than “noun”, although our approach can also apply tonoun’s diﬀerence.

′

Decoder:the resulting hidden representation y is thenmapped back to a reconstructed d-dimensional vector z:

Suppose we have two image sets, one of “subjective ad-z = fθ′ (y) = σ(W′ y + b′ ) with θ′ = {W′ , b′ }. The twojective noun” and one of “noun”. They are two candidate

parameter sets are usually constrained to have a tied weights

images of relevant images and irrelevant images. Our pur-between W and W′ : W′ = WT .

pose is first to find truly relevant images and truly irrele-

The parameters are optimized to to minimize an appro-

vant images and then using these images to extract usefulpriate cost function (e.g. measure square error) over thefeatures that can represent the input “subjective adjective

training set.

noun” query. To better find discriminative features that are

2. 2 Denoising Auto-Encoder

especially eﬀective to “subjective adjective” of the “noun”,
we compare these two image sets in a pairwise way. Without any labels that indicate whether diﬀerences of an image
pair contains discriminative features, we propose to use unsupervised method to find truly relevant and truly irrelevant
images first and then estimate relevance of an image to a

Figure 2 The denoising auto-encoder architecture.

“subjective adjective noun” query.
Performance of feature representation is limited by the rep-

In order to make the trained representation robust to par-resentation power of handcrafted features if we extract fea-

tial destruction of the input, Vincent et al. [15] proposed de-tures like SIFT, HOG first and then learn image similarity,

noising auto-encoders by introducing a corrupted version ofand neural network has shown its advantages in many re-

the input. As showed in Figure 2, during the encoder andsearches [8] [10] [1]. In this research, we proposed a stacked

decoder process, the representation y is trained on the cor-pairwise convolutional auto-encoder by borrowing the idea

rupted input x̃ instead of original input x, while the costof convolutional auto-encoder (CAE) from Masci et al. [11].

function is measured between the reconstructed z and the

Ideally, our approach will be able to learn representative fea-

uncorrupted input x.

2. 3 Convolutional Auto-Encoder

relevance of an image to the query.

To deal with 2D image structure with auto-encoder andMost search engines return images based on the relevance

reduce redundancy in the parameters brought by global fea-

between the query and image’s contextual contents whiletures, convolutional auto-encoder (CAE) is proposed [11].

not all images are exactly corresponding to their surround-

The weights are shared among all locations in one feature

ing contents. As a result, it is not guaranteed that most

map of a channel and the reconstruction is a linear combi-

resulting images are relevant images in consider of a “sub-nation of basic image patches based on the latent code.

jective adjective noun” query, and we call these result im-

For the input x of k-th feature map (0 < k ⩽ H, H isages as pseudo-relevant images. We can also find im-

the number of latent feature maps), the representation isages from existing searching engines with the query thatcomputed as y = σ(x ∗ W + b ). Here σ is an activation

includes the “noun” keyword and excludes the “subjectivefunction and ∗ denotes the 2D convolution. The bias bk is

adjective” keyword, and these result images are denoted

broadcasted to the whole map. The reconstruction is ob∑
tained with: z = σ( k∈H yk ∗ W̃k + c). W̃k denotes the flip

as pseudo-irrelevant images. By applying unsupervised

operation over both dimensions of the weights.

tional stacked convolutional auto-encoders, it is able to learn

k

k

k

learning method to pseudo-relevant images, such as tradi-

Mean squared error (MSE) between the input x and re-representative feature for both “subjective adjective” andconstructed z is used to measure the cost function that is be“noun”. However, features of “noun” are usually more sig-

minimized. As in the standard neural networks, the back-nificant than features of “subjective adjective”. As a result,propagation algorithm is applied to compute the gradient of

we propose to compare pseudo-relevant images with pseudo-

the cost function with respect to the parameters. A max-

irrelevant images to better learn discriminative features forpooling layer is used to obtain translation-invariant repre-“subjective adjective” of a certain “noun”. Our assumption

sentation.

is that:

2. 4 Stacked Auto-Encoder［Assumption 1］ Discriminative features that help add the

Deep networks can be trained by building several auto-

quality of “subjective adjective” are similar for one object

encoders in a layer-wise way [2]. The representation of the(“noun”) in certain dimensions.

n-th layer is used as the input for the next (n + 1)-th layerWith this assumption, we can know that diﬀerences thatand the (n + 1)-th layer is trained after the n-th has been

represent discriminative features are similar while diﬀerences

trained. This pair-wise greedy procedure has shown signifi-

of other features are not similar. As a result, we can learn

cantly better generalization on a number of tasks [9].

the discriminative features of image pairs with some unsu-

3. Approach
The final goal of this research is to estimate the relevance

pervised method.
We use P = {p1, p2, p3...}, |P | = m to denote top m
search result images of “subjective adjective noun” queryof an image to a given “subjective adjective noun” query.

and Q = {q1, q2, q3...}, |Q| = m to denote top m search re-

An image is relevant to a “subjective adjective noun” querysult images of “noun” query. Here P denotes pseudo-relevant

when the content of the image includes exactly the “noun”

image set and Q denotes pseudo-irrelevant image set.

(e.g. sky, cat, people) in the query and we can feel the qual-

The main approach consists of four parts:

ity of the “subjective adjective”(e.g. blue, cute, happy) from（ 1 ） Make image pairs from pseudo-relevant image set

the image as well.

and pseudo-irrelevant image set,

Intuitively, the problem of measuring the relevance of an（ 2 ） Learn discriminative feature to represent diﬀerences

image to an object (“noun”) is similar to object recognition

between truly relevant images and truly irrelevant images

problem. However, when object and subjective adjective arefrom pseudo-relevant image set and pseudo-irrelevant image

combined to be measured, the problem becomes complicated.

set,

As we have explained in the first section, features that make（ 3 ） Use the learnt discriminative features to cluster im-

an image relevant to a subjective adjective and noun will

age pairs and find images pairs that include truly relevant

depends on both the adjective and the noun. Traditional

image and truly irrelevant image,training ways that try to learn useful features with super-（ 4 ） Utilize the discriminative features and truly relevant

vised methods require a large number of images labeled withimages to learn a function that can measure the relevance ofground truth subjective adjectives and nouns. However thisan image to the “subjective adjective noun” query.

is unrealistic for image searching problems. Thus, we pro-In this paper, we will have a detailed explanation of howpose to apply unsupervised approach to learn truly relevant

we make image pairs, learn discriminative features of sub-training dataset first, and then use the dataset to estimate

jective adjective from images with pair-wise stacked convo-

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Figure 3 The framework of pair-wise stacked convolutional auto-encoders architecture.

lutional auto-encoders, and then learn truly relevant images

architecture of stacked convolutional auto-encoders to makewith discriminative features.

it suitable to learn representative diﬀerences between pseudo-

3. 1 Image Pair Construction

relevant image set and pseudo-irrelevant image set. Figure 3

In order to decrease the side eﬀects of many noisy diﬀer-

shows the architecture of our pair-wise stacked convolutional

ences between two images, we first conduct a simple image

auto-encoders.

pair selection from the pseudo-relevant image set and theSuppose we have one image pair (a, b), a ∈ P, b ∈ Q. The

pseudo-irrelevant image set. Since the discriminative fea-

encoding and decoding process is similar to the convolutionaltures we aim to find are more about subjective adjectives,

auto-encoder explained in Section 2. 3. Both images a and b

objects play a less importance in the diﬀerence between aare passed through the network. The whole network consists

truly relevant image and a truly irrelevant image.

of two main parts, encoding part in the first half and decod-

We utilize state-of-art object detection networks faster R-

ing part in the second half. The encoding part includes sev-

CNN [13] to detect objects in images of two dataset and con-eral encoding process. And the decoding part has the samestruct our image pairs to include two images with similar

number of decoding processes with each one corresponding

objects in each image set.

to the encoding process respectively. As we can see from

3. 2 Pair-Wise Stacked Convolutional Auto-Encoders Figure 3, suppose we have three processes in the encoding
As we has explained in the above section, we can apply un-

part: Encoding process # 1, Encoding process #2, and En-

supervised learning method to learn discriminative featurescoding process #3. The output of lower process serves as thethat represent diﬀerences between truly relevant images andinput of next process. In the decoding part, we have three

truly irrelevant images. To better learn discriminative fea-

decoding processes in a stacked way. In other words, the firsttures for “subjective adjective” of a “noun”, we modify the

decoding process is corresponding to the most upper encod-

ing process and the last decoding process is corresponding to

3. 3 From Pseudo-Relevant Images to Truly Relevant Images

the first encoding process.
The upper part of Figure 3 explains the detailed work-

With the pair-wise stacked convolutional auto-encoders,flow of each encoding process and decoding process. Eachwe will learn feature representations for only “subjective ad-

encoder consist of a convolutional layer to map the input

jective” of a certain “noun”. With these significant features,to several feature maps with diﬀerent kernel (convolutional

we can do clustering to the training image pairs. The simi-

matrix) and a max-pooling layer for spatial down-sampling.

larity between these image pairs are computed by Euclidean

The decoder includes an up-sampling layer and a deconvolu-

metric between their diﬀerences’ feature representations. We

tional layer. In traditional auto-encoder systems, the output

apply K-means algorithm for clustering. The largest clusterof the decoding process is compared with the input of the en-

are treated as image pairs that include a truly relevant image

coding process and the training process is repeated to makeand a truly irrelevant image.

them as similar as possible. As a result, the representative
features are learnt to reconstruct the input image as well as

4. Experimentpossible.
In our pair-wise auto-encoder, we are supposed to find therepresentative diﬀerences between sets of image pairs. Thus,
instead of computing cost function with reconstructed out-

Table 1 The queries we used in the experiment. (Ratio A: ratioof truly relevant images in pseudo-relevant images, Ratio B: ratio of truly relevant images in pseudo-irrelevant
images)put and original input, we compare diﬀerences of the recon-

Query

Ratio A Ratio B

structed output with diﬀerences of original input. As we canhappy dog

0.785

0.18

see from Figure 3, for two images a and b in the image pair,tiny flower

0.688

0.3

the input of the k-th encoding process are denoted as ika andclear sky

0.565

0.2

ika

ancient city

0.865

0.2

falling snow

0.735

0.25

warm water

0.425

0.075

happy kids

0.83

0.3

dry flower

0.81

0.055

fluﬀy clouds

0.899

0.675

respectively.

We use d(ika , ikb ) to define the diﬀerence of

these two images’ input before passing them into the k-th
encoding process through our network:d(ika , ikb ) = ika − ikb .
Similarly, we have oka and okb to represent the feature repre-

fresh flowers 0.78

0.6

sentation (reconstructed output) after k-th decoding process
for image a and image b through the network, and their dif-In the experiment, because of the restricted images crawl-

ference are denoted as d(oka , okb ). We then define the squared-

ing from the search engines (not allowed to crawl or a very

error loss between the two diﬀerences in k-th encoding pro-limited number of permission), we decided to use existingcess and k-th decoding process:dataset that used in [3]. One advantage of using this dataset
is that with the labels for each images, we do not need to

Lk (a, b) = M SE(d(ika , ikb ), d(oka , okb )).
And the total loss is weighted sum of mean square errors inall corresponding encoding-decoding processes:
total

L

=

∑

spend extra cost to evaluate whether an image is relevant toa query or not in the evaluation phase. The images in thedataset are from Flickr and the dataset include 1553 ANPs
(Adjective Noun Pairs) with their images. In order to make

k

k

w L ,

k∈(1,n)our dataset, we clustered all the ANPs based on nouns. The
we selected ten queries (ANPs as called in their research)where wk are weighting factors of the contribution of eachwith nouns that have many adjectives in the cluster. We also

process to the total loss. The backpropagation algorithm isconsidered the number of images for the queries to make sureapplied to compute the gradient of the error function withthat each query have more than 1000 images. Table 1 lists

respect to the parameters.

all the queries we used in the experiment.

The representative features that learnt from the convolu-

To better simulate the ratio of truly relevant images intional auto-encoder can be used as the input of next auto-

the pseudo-relevant image set and pseudo-irrelevant image

encoder process. After the weights are fine-tuned with back-set as in the real search engines, we conducted a survey of

propagation, the top level activations can be used as thethese ten queries in web image search engines (Google andfeature representation for further supervised learning.

Flickr). For each query, we surveyed the ratio of truly relevant images to the query in the top 200 result images with

Table 2 The size of input and output data in each layer of the network.
Process
Encoding Process #1
Encoding Process #2
Decoding Process #2
Decoding Process #1

Layer

Type

Input

FilterOutputLayer 1 Conv

100 × 100 × 3 5 × 5 × 3 × 20

96 × 96 × 20

Layer 2 Pool (max)

96 × 96 × 20

2×2

48 × 48 × 20

Layer 3 Conv

48 × 48 × 20

5 × 5 × 20 × 20 44 × 44 × 20

Layer 4 Pool (max)

44 × 44 × 20

2×2

22 × 22 × 20

Layer 5 Up-sampling 22 × 22 × 20

2×2

44 × 44 × 20

Layer 6 Deconv

44 × 44 × 20

Layer 5 Up-sampling 48 × 48 × 20
Layer 6 Deconv

96 × 96 × 20

5 × 5 × 20 × 20 48 × 48 × 20
2×2

96 × 96 × 20

5 × 5 × 20 × 3

100 × 100 × 3

both pseudo-relevant images (results of the subjective ad-

are not really truly relevant images and truly irrelevant im-

jective noun query) and pseudo-irrelevant images (results ofages. In that case, we consider to make our own dataset thatthe noun query). We took the average number as the simu-

can perfectly match our research goal, such as sequence of

lation ratio as showed in Table 1. For each query, images ofimages for the same objects. Parameter is a very importantthe query are treated as truly relevant images and images offactor to influence the performance of deep neural network

other ANPs with the same noun are treated as truly irrele-

and we will need more trials to adjust them to make better

vant images (we also filtered some ANPs that have very sim-

performance. Moreover, we will try to get visual represen-ilar adjectives, such as “excited kids” for “happy kids”). The

tation of the learnt features to have a better and intuitive

pseudo-relevant image dataset and pseudo-irrelevant image

understanding of what we have learnt with the network.

dataset were constructed by adding images from the truly
relevant images and truly irrelevant images with their num-

6. Conclusionbers fit the ratio we surveyed in real image search engines.

In this paper, we propose to solve the problem of esti-

In our experiment, we had two encoding processes andmating relevance of images to “subjective adjective noun”

two decoding processes. We set the batch size of image as

queries by first learning trustful relevant images with unsu-

100 × 100. Size of input and output in each layer is shown

pervised deep convolutional auto-encoders and then learn toin Table 2 as well as the shape of each layer. For exam-measure the relevance. We propose pair-wise stacked con-ple, the first convolutional layer used 20 kernels to filter thevolutional auto-encoders to find discriminative features that

100 × 100 × 3 input images of the size 5 × 5 × 3. The output

can represent diﬀerences between relevant images and irrel-of the first convolutional layer is processed after pooling andevant images. We show our conducted experiment and thethen taken as input of the second convolutional layer (alsoresult is compared with precision of some image search en-

the input of the second encoding process).

gines. Finally we make a discussion according to the result
and we list some future plans.

5. Result and Discussion

7. Acknowledgement
Table 3 Result of our approach and the precision of top 200 inimage search engines for two queries.
Query

Precision@200* Accuracy of ours

happy dog

0.565

0.617

clear sky

0.785

0.802

This work was supported in part by Grants-in-Aid for
Scientific Research (Nos. 15H01718, and 26700009) from
MEXT of Japan.

* the mean precision of top 200 in image search engines (Google image and Flickr)

Table 3 shows comparison of our approach and the mean
precision of top 200 in image search engines (Google image
and Flickr) for two queries. We can see that our approach
could slightly outperform the current image search engine
when the query is a “subjective adjective noun” query.
We consider much more space for improvement in this research. In the future, we will take consideration of similar
subjective adjectives when getting pseudo-relevant images
and pseudo-irrelevant images. Images in the dataset we use

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