IMAGE CONTENT DESCRIPTION USING LSTM APPROACH

Dhinaharan Nagamalai et al. (Eds) : SIGEM, CSEA, Fuzzy, NATL - 2017
pp. 01– 12, 2017. © CS & IT-CSCP 2017 DOI : 10.5121/csit.2017.70901
IMAGE CONTENT DESCRIPTION USING
LSTM APPROACH
Sonu Pratap Singh Gurjar1
, Shivam Gupta1
and Rajeev Srivastava2
1
Student, Department of Computer Science and Engineering,
IIT-BHU, Varanasi, Uttar Pradesh, India
2
Professor, Department of Computer Science and Engineering,
IIT-BHU, Varanasi, Uttar Pradesh, India
ABSTRACT
In this digital world, artificial intelligence has provided solutions to many problems, likewise to
encounter problems related to digital images and operations related to the extensive set of
images. We should learn how to analyze an image, and for that, we need feature extraction of
the content of that image. Image description methods involve natural language processing and
concepts of computer vision. The purpose of this work is to provide an efficient and accurate
image description of an unknown image by using deep learning methods. We propose a novel
generative robust model that trains a Deep Neural Network to learn about image features after
extracting information about the content of images, for that we used the novel combination of
CNN and LSTM. We trained our model on MSCOCO dataset, which provides set of annotations
for a particular image, and after the model is fully automated, we tested it by providing raw
images. And also several experiments are performed to check efficiency and robustness of the
system, for that we have calculated BLUE Score.
KEYWORDS
Image Annotation, Feature Extraction, LSTM, Deep Learning, NLP.
1. INTRODUCTION
The image is an important entity of our digital system. It contains much useful information like an
image of a receipt, an image taken from CCTV footage etc. We can surely say that an image tells
a unique story in its way. In today’s digital world, one can perform or gather a large information
or facts just only after analyzing a digital image. When we are dealing with digital images, we
have to gather what each and every part of it wants to contain. For that, we should extract each
and every part with optimal care and extract information of that particular region and then gather
the whole information to reach out to a conclusion. Here we need to get what the picture have like
objects, boundaries, and colour etc. features. Here, we need an accurate description of an image
and for digital images, we need an efficient and accurate model that could give accurate
annotations of each and every region of that image and can provide a rich sentence form, so that
we can understand what’s happening in that image.
Image captioning[1] is used by several software giants companies like Google, Microsoft etc. It is
used for many other specific tasks like explaining an image for a blind person by giving him a
sentence generated form of annotations of an image. Such essential and significant functionalities
make image captioning an important field to study and explore.

2 Computer Science & Information Technology (CS & IT)
Image annotations generation of an image is very much close to scene understanding model.
Computer vision involves the complete understanding of an image, so our model should not only
just provide image annotations, but should be capable of expressing the scene and what exactly
objects are doing in that image. In this way, computer vision and natural language processing go
hand to hand for solving this problem of automatic image description by providing suitable
generated sentence explaining the scene of an image. Likewise, a human can easily perceive the
content of an image just by looking at it, and he can explain the scene in that image accurately.
But, when it comes to the computer, it’s a difficult task to generate and explain scene of an image
by using machine learning algorithms. Human generated annotations have properties like rich,
concise and accurate etc. Like, a human can generate a well fit sentence, and that sentence has
only relevant things and accurate as it contains all essential region’s information of an image.
Many researchers [1, 14] and others have explored this problem and provided few appropriate
solutions. There are many advances in this field as large datasets like MSCOCO, Flickr30k etc.
are available to train the model more efficiently. The basic model (as shown in figure 1.) for
image captioning works like, first gather the features of an image and given captions in the
dataset, and based on features provide a suitable annotation to that image. In figure 1, it shows a
man lying on the table and a dog sitting near him. As this is a sample input image firstly its
features based on colour, objects, boundaries, texture etc. is extracted and also features of given
captions, then based on its attributes a common representation is produced. And from that
common space embedding representation, an appropriate sentence is generated for this image like
a man lying on the table with a dog.
Figure 1. Basic working of Image Captioning model
Most of the works in this field are based on these two approaches: similarities approach and
constructive approach. Similarities approach [1], [2], [5] and [6] means taking the model as a
retrieval task, after extraction of features of image and captions, this approach provides an
embedding representation of information and based on that most suitable annotation is selected as
annotation for an image. This approach has few limitations like it doesn’t provide good results
when a raw image contain an unseen object or thing in it, as due to the limited size of its
dictionary, it produce annotations based on previously gathered features and language models. In
this way, this approach is not suitable in today’s advance in this field. Constructive approach [3],
[4], [7], [8], and others mean the generation of sentence based on firstly learning image features
and after that sentence generation process occurs in many parts. Like a basic constructive
approach based model consists of these basic steps: language modeling part, image features
extraction and analysis part, and representation part which combines the previous both parts. In
[4], they described an image by using the constructive approach as Convolutional Neural
Network (CNN) is used for extraction of image features and after that Recurrent Neural Network

Computer Science & Information Technology (CS & IT) 3
(RNN) is used to learn the representation of space embedding and generated a suitable sentence
for an image. Here, language modeling means, it learns dense features information for every word
related to the content of that particular image present in the dictionary and it gathers its semantic
form in recurrent layers.
In [1], Pan et al. used similarities approach and found similarity measures between the captions
keywords and the images. They described an image region in form of blob-tokens, it means a part
of image region based on its features like colour, texture, size, position, boundaries, and shape.
And [2], applied nearest neighbor approach along with similarities measures, as a set of keywords
which are nearer to each other form the sentence. These models have some limitations like they
are biased with training dataset, and doesn’t give a good result for a new unseen image. In [3,4],
they proposed a model based on CNN[3] with RNN[11] method, as this model is based on
constructive approach and it produced some good results as compared to models defined with
similarities approach. These models provide better scene understanding and able to express the
content of the image semantically. But they has few limitations like over fitting of data and not
able to give good results when attention is not given to similar type of images.
Our novel work involves most optimal technique with the constructive approach, as we used
CNN method to extract image features and then the common representation of whole information
and features of images and captions are made by using LSTM[15] model, which then produce
appropriate sentence for any new image. Our model produces accurate annotations and also the
sentence expresses the scene of the image, along with information about all objects and stuff in
that image. Our contributions are as follows:
• Image feature extraction is done using robust CNN technique, which produces features
based on colour, texture, and position of objects and stuff in the image.
• The common representation contains all gathered information in layers and cells of
LSTM model and then based on input, hidden layers and then final output from output
layers are obtained.
• For language modeling, we used n-gram model, so that accurate and rich form of a
sentence is generated.
• Blue-4 metric is used for analysis of efficiency and robustness of our method. And the
comparison of various previous models with our model.
A further portion of the paper is divided into sections as follows, Section 2 included related work
portion which gives detailed information about previous works done in this field. Section 3
provides the complete overview of the technique of image captioning such as problem statement,
input and output details, and also includes the motivation behind this work, and all relevant
terminology and concepts are discussed in detail. After that our model is described in Section 4,
then Section 5 give results and implementation details as dataset used are MSCOCO, Flickr30k,
and Flickr8k, also the value of BLUE-4 metric score is provided as the efficiency measure of our
model. Section 6 discuss the conclusion and future improvements in this work.
2. RELATED WORK
There are many advances in the field of automatic image captioning. As in earlier works, [1] Pan
et al. proposed technique which work by annotating a particular part of image region, in this a
word for each image region and then on combining we get the sentence, they discussed about
considering the image regions as blobs-token which means an image regions based on its feature
such as colour, texture, and position of object in image. But this technique has few limitations

such as it is effective only for a small dataset, this work include much manual work as they have
to provide annotated words and blob-tokens with an image, and this approach sometimes give
results based on a training set, that means it is biased on a training set.
Jacob et al. [2], provided technique that explores nearest neighbor images in training set to the
query image and based on that appropriate k nearest neighbor images, their given captions are
imported and based on that only caption is given for the query image. But there is a limitation of
this approach as it performs better for highly similar images, but worse for highly dissimilar
images. In [5], they used similarities approach as a candidate matching images with the query
image are retrieved from the collection of captioned images, then after features are matched and
based on the best rank obtained a caption is given to the query image. But this model has few
limitations like re-ranking the given captions could create error for training images and related
text, and also object and scene classifiers could give erroneous results, so the model could have
given faulty results. In [6], Ali et al. proposed model based on computing of a score linking a
sentence with an image. And worked based on the semantic distance between some words like
two or three for a particular image, and SVM models are trained on it. This model lacks as dataset
used is not much used and large, and not much emphasized for checking adjectives and other
relevant potential good information from image regions.
In [3], [4], and [7,14], they used the constructive approach, but with different techniques for
extracting images and then after that for sentence generation. Karpathy et al. [3], described the
common intermodal representation between the visual information and the language models.
They used the combination of CNN over image regions and Bi-directional Recurrent Neural
Network (BRNN) for sentence generation approach by computing word representation. But this
model also have certain limitations as this model didn’t focus on attention concept for captioning.
In our model, CNN is used for extraction of features of an image, and it provided that information
to the common representation. In [4], this paper focussed on tight connection between image
objects and text related to that. As they acquired every detailed information of each and every
region of an image by particularly dealt with each region, as they extracted and detected
objects/stuff in an image, and their attributes such as adjectives which provide extra useful
information about that particular region, also details about the spatial connections between those
regions, and based on these Conditional Random Field (CRF) is constructed and labels of graphs
are predicted, and finally based on these labelling sentence is generated. This model contains few
limitations such as it didn’t provide semantically related texts on input images, so the accuracy of
the model is compromised in this way.
Most of the work in this field is related to providing a visual interpretation of the image and
relation to that to given captions in the dataset. In [7], Desmond et al. introduced representation to
contain connections between different objects/stuff of an image, it worked based on similarities
between the objects or image regions and based how these regions relate to each other. They used
image parser to get information for each region of an image. But this model have certain
limitations as the output of an object detector is not used to obtain a fully automated model, and
there are various improvements that can be done to the image parser to enhance the efficiency of
this model.
3. OVERVIEW OF METHOD
Image captioning involves machine learning algorithms and as well many mathematical
simulations so that an accurate annotation can be provided.

Computer Science & Information Technology (CS & IT)
3.1. Motivation
As human can perceive an image just by looking at it, we must have a robust and accurate model
that can cope up with a human in case of captioning an image and express what the objects are
doing in that image. Our model should have all im
sentence generation, consistent as could not be a biased model and concise as it much includes
only relevant regions of the image. And there are many real life applications of image captioning
like image search, tell stories from the pictures uploaded on the web and helpful for visually
impaired people so that they can be aware of relevant information from the web.
3.2. Problem Statement
Image content description by using the neural networks and concepts of dee
3.3. Input
A query image.
3.4. Dataset
MSCOCO, Flickr30k, and Flickr8k.
3.5. Output
Captioning of that query image, according to the learning of the model.
3.6. Feature Extraction
For a query image, firstly we extract its features based on its
stuff, and position of things in it. This process of feature extraction is very crucial in our model,
as it provides all basic required information about the image. This process is done with help of
CNN models. As CNN contains certain specified number of layers and those layers are
responsible for storing the features which are extracted from the images, then these features are
passed forward to LSTM model so make a common representation for sentence generation. As
shown in figure 2, an image is given to the model, and then model extracts all relevant features of
that image such as:
• Low-level features, it involves around details of pixels level such as the colour of a pixel
and edges and corners in images.
• Mid-level features, it involves between low
curves in the image of an object present in it.
• High-level features denote detection of the object in the image. It is hard to predict the
exact object and scene of that object
minimizing the gap between this low
Figure 2. An image and extraction of its features by CNN
Computer Science & Information Technology (CS & IT)
doing in that image. Our model should have all important characteristics like accuracy, rich in
h, tell stories from the pictures uploaded on the web and helpful for visually
impaired people so that they can be aware of relevant information from the web.
Image content description by using the neural networks and concepts of deep learning.
MSCOCO, Flickr30k, and Flickr8k.
Captioning of that query image, according to the learning of the model.
For a query image, firstly we extract its features based on its colour, texture, boundaries, objects,
contains certain specified number of layers and those layers are
wn in figure 2, an image is given to the model, and then model extracts all relevant features of
level features, it involves around details of pixels level such as the colour of a pixel
and edges and corners in images.
atures, it involves between low-level and high-level features, it discuss any
curves in the image of an object present in it.
level features denote detection of the object in the image. It is hard to predict the
exact object and scene of that object in that image, so image captioning is all about
minimizing the gap between this low-level and high-level features methods.
Figure 2. An image and extraction of its features by CNN
5
portant characteristics like accuracy, rich in
h, tell stories from the pictures uploaded on the web and helpful for visually
p learning.
colour, texture, boundaries, objects,
contains certain specified number of layers and those layers are
wn in figure 2, an image is given to the model, and then model extracts all relevant features of
level features, it involves around details of pixels level such as the colour of a pixel
level features, it discuss any
level features denote detection of the object in the image. It is hard to predict the
in that image, so image captioning is all about

After obtaining all relevant features, CNN gives it to a trainable classifier. And from that to the
common representation model. It is a neutral network which is fully trainable with the help of
mathematical simulations like stochastic gradient descent.
The model takes an input image and provides the possible caption C from the available dictionary
of 1-to-T words, such as:
C = C1, C2, …, CN, where Ci ϵ RT
(1)
, where T equals to the available number of keywords of the vocabulary and N is the possible
length of the sentence.
We use CNN to extract the image features as a set of feature vectors. Then it produces M vectors,
which belongs to a part of the image and having an L-dimensional form, such as:
B = B1, ….., BM, Bi ϵ RL
(2)
As shown in figure 2, CNN extracts features from lower levels, so that each relevant region is
completed for sentence generation. So that decoder, LSTM model could focus on only relevant
portions of the image by using feature vectors, r(I) for an image I having a specified dimension.
CNN_MODEL(I) = Wi
r(I) + b (3)
As per the measurement of accuracy of the features extracted by our model, we trained our model
based on visualization parameters, which helps in examining of the different feature activations
and their relation to features embedding. Also, our model worked on both image classification
and the localization tasks. It is analysed that as the network grows, there is rise in the number of
filters used. So, the accuracy is optimised by incoporating more number of filters.
3.7. Language Modeling
In our model, we have used n-gram model for language modelling, it means a statistical
probability function based on conditional factors such as for N words =
P(ai | ai-N+1 , …., ai-1) (4)
, it means the possibility of the next word of the sequence is based on the previously occurred
words in the sequence.
Figure 3. Basic Framework of our model.

3.8. LSTM
Long Short-Term Memory (LSTM) [15,16] acts as the decoder in our model when features are
transferred to it, it uses the common representation of all gathered information and the based on it
provide sentence. As shown in figure 3, the basic framework of our model is depicted, as shown
an image is provided as an input to our model, then CNN used for feature extraction and then
extracted features are given as input to the LSTM unit, which finally generates sentence, as
shown in figure 3, which provides the basic framework of our model.
LSTM model generally consists of three important gates such as input gate, forget gate and output
gate. And the main part of an LSTM model is its memory cell c, which keeps the whole
information about the image features, previously generated words and track functionality of all
three gates.
3.9. Words Representation
It is based on the size of the vocabulary of our model, like we taken image dimension as ID =
4096, so word form will become of order:
T x ID (5)
4. DESIGN OF PROPOSED METHOD
In our model, image features are extracted by CNN, then LSTM model acts like a decoder of that
features. As CNN_MODEL(I) is passed to LSTM model, as an input. It takes it, and further
evaluate values of gates defined in its inner working system. And whole working information of
the system is stored in memory cells, c. These gates units are trained to learn when to open and
close permit of access to information to memory cells. Three gates used as whether the current
cell value is to forget(forget gate f), input gate (i) is to read input and output gate (o) to whether
output the new cell value.
LSTM model computes on the basis of the memory cell information and previously calculated
words of the sequence, such as:
P(St|I,S0 , …., St-1) (6)
, where I is an image and S is a possible sentence which depends on previously generated words.
These are the main equations which explains our model:
at-1 = CNN_MODEL(I) (7)
at = We St (8)
pt-1 = LSTM_MODEL(at ) (9)
, where at means input to LSTM model, as CNN_MODEL(I) is initial input to LSTM model, and
then after it works recursively and obtain one word of sentence at each time.
Updating of gates and cell values in a LSTM model as such:
it = ơ (Wia at + Wik kt-1) (10)
f = ơ (Wfa at + Wfk kt-1) (11)

o = ơ (Woa at + Wok kt-1) (12)
c = ft ʘ ct-1 + it ʘ h(Wca at + Wck kt-1) (13)
kt = ot ʘ ct (14)
pt+1 = Softmax(kt ) (15)
where, ʘ means multiplication of a gate value, Softmax() is used as for higher dimension
balancing between the various stages of values. And the pair ( kt , ct) is passed as the present form
of hidden state to the upcoming hidden state. And kt is given to Softmax, which provide a
probability distribution.
As shown in figure 4, LSTM models work recursively after a word is found, and use that
information to predict the next word of the sentence.
Figure 4. Working on LSTM model for sentence generation.
4.1. Sentence Generation
In our model, LSTM is used for sentence generation, the process of sentence generation involves
certain basic steps, such as it starts from “##START##” or any other sentence generation
reference words, which conveys that next word that will be generated will be the first word of our
desired sentence. Our method calculates the probability distribution for the upcoming word,
P(St|I,S0 , …., St-1). After that we use this distribution method and previously calculated words for
the calculating probability of the next word. And cycle goes on until we encounter the last word
of sequence and then after model produce output as end sign “##END##”. We use our model to
calculate the probability of generating a sentence given an image. The sentence generation task is
incorporated by using the perplexity of a sentence conditioned on the averaged image feature
across the training set as the reference perplexity to normalize the original perplexity as discussed
in [8].
For an example, in figure 5, an input image is given, our model starts predicting a word at a time
and by using the previously calculated word, with the further calculation of probability
distribution, it predicts the next word. Like first word predicted based on the image features is
“man”, then by using it and model parameters, it predicts the next word as “bench”, and process

goes on until the model encounters the stop word. In this way, LSTM generates the most optimal
sentence for a new input image.
5. IMPLEMENTATION AND RESULTS
Our model includes the novel combination of CNN and LSTM techniques with using deep
learning approaches. We test our method on benchmark datasets like MSCOCO [12], Flickr30k
[13] and Flickr8k [14]. The dataset MSCOCO contains around 80k images, and each image has at
least 5 different captions of different lengths related to it. This contains images of almost
everything such as sports, landscapes, portraits, persons, groups etc. Out of 80k images, we have
taken 5k images for testing phase and check the implementation of our model on that testing
dataset.
We used deep learning approach for implementation of our model, and it is implemented in
python by using Keras [17] library, a high-level neural network for fast and accurate
implementation which is run on Theano as backend.
5.1. Input
Human caption: A man lying on the bench and a dog sitting on the ground.
Figure 5. An input image
5.2. Output
Caption generated by our model: A woman sitting on a bench with a dog.
Figure 6. Output: A woman sitting on a bench with a dog.

5.3. Metric
We have used BLEU-4 metric instead of BLEU-1 metric, as it is a far better metric for measuring
the efficiency of our model.
BLEU-4 metric score of our model: 20.84
Figure 7. BLUE-4 Score: 20.84.
5.4. Parameters for Implementation
Our LSTM model is implemented by using 2 layers, as we have checked it with 1 layer too. But
former method produces more optimal captions. We have observed that on increasing further
number of layers, generated captions efficiency degraded. So, we concluded that a number of
layers are 2, is the most optimal method for implementation. And weights are initialized
uniformly from [-0.06, 0.06].
We have taken maximum caption length = 16.
Batch size = 200
Dimension of LSTM output = 512
Image dimension parameter = 4096
Word vector dimension = 300

5.5. Comparison
We have compared our model with state-of-art techniques [1, 2, 3, 4], and based on BLEU-4
metric.
Table 1. Comparison of models
Dataset Model BLEU-4 Score
MSCOCO Random 4.6
MSCOCO Nearest
Neighbour[2]
9.9
MSCOCO CNN & RNN[8] 19.5
MSCOCO Karpathy[3] 20.4
MSCOCO Human 20.51
MSCOCO Our model 20.84
From Table 1, we can see that our model is far better and efficient than previous works done in
the field of automatic image captioning.
6. CONCLUSIONS
Our novel method showed that it is an efficient and a robust system, and can produce the
description of any unseen image, which is more specific or related to the content of that image.
And, it also is shown that our model is much better than state-of--art models and others previous
automated works. As the measure of the efficiency of our model, we calculated BLEU-4 metric
which is around 20.84 for our model. Several experiments are performed on different datasets,
which depicts the robustness of our method.
In future works, we can make the current model more fast and efficient by applying fast machine
learning algorithms. Also, we can fine-tune features extracted by CNN to improve correctness of
our model. Also, we can test our model on more number of testing the dataset for better results.
ACKNOWLEDGEMENTS
The authors would like to thank Department of Computer Science and Engineering, IIT-BHU,
Varanasi for providing wonderful opportunity and complete facility for research works.
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