- Open Access
- Authors : Shahar Banu , Seemakousar B , Sanchita S M , Nivedita A, Arun Joshi, Rajeshwari S.G
- Paper ID : IJERTV10IS080198
- Volume & Issue : Volume 10, Issue 08 (August 2021)
- Published (First Online): 06-09-2021
- ISSN (Online) : 2278-0181
- Publisher Name : IJERT
- License: This work is licensed under a Creative Commons Attribution 4.0 International License
Deep Learning based Automatic Image Caption Generation
Prof. Rajeshwari S.G, Prof. Arun Joshi, Nivedita A, Sanchita S M, Seemakousar B, Shahar Banu
Department of Computer Science and Engineering, SKSVMACET, Laxmeshwar, Karnataka,
Abstract The paper aims at generating automated captions by learning the contents of the image. At present images are annotated with human intervention and it becomes nearly impossible task for huge commercial databases. The image database is given as input to a deep neural network (Convolutional Neural Network (CNN)) encoder for generating thought vector which extracts the features and nuances out of our image and RNN (Recurrent Neural Network) decoder is used to translate the features and objects given by our image to obtain sequential, meaningful description of the image. In this paper, we systematically analyze different deep neural network-based image caption generation approaches and pretrained models to conclude on the most efficient model with fine-tuning. The analyzed models contain both with and without attention concept to optimize the caption generating ability of the model. All the models are trained on the same dataset for concrete comparison.
Keywords Automated captions, deep neural network, CNN, RNN, feature extraction, attention.
A large amount of information is stored in an image. Everyday huge image data is generated on social media and observatories. Deep learning can be used to automatically annotate these images, thus replacing the manual annotations done. This will greatly reduce the human error as well as the efforts by removing the need for human intervention The generation of captions from images has various practical benefits, ranging from aiding the visually impaired, to enabling the automatic, cost-saving labelling of the millions of images uploaded to the Internet every day, recommendations in editing applications, beneficial in virtual assistants, for indexing of images, for visually challenged people, for social media, and several other natural language processing applications. The field brings together state-of-the-art models in Natural Language Processing and Computer Vision, two of the major fields in Artificial Intelligence. One of the challenges is availability of large number of images with their associated text ever- expanding internet. However, most of this data is noisy and hence it cannot be directly used in image captioning model. For training an image caption generation model, a huge dataset with properly available annotated image is required. In this paper, we plan to demonstrate a system that generates contextual description about objects in images. Given an image, break it down to extract the different objects, actions, attributes and generate a meaningful sentence (caption/description) for the image.
Fig. 1. Image captioning task
Generating captions automatically from images is a complex task as it entails the model to extract features from the images and then form a meaningful sentence from the available features. Basically, the feature extraction is done by training a CNN (Convolutional Neural Network) with huge number of images and the correct weights are identified by multiple forward and backward iterations. With the help of RNN (Recurrent Neural Network) and the extracted features, a sentence is generated. Figure 2 shows the block diagram.
Fig. 2. Block Diagram without attention
approximate variational lower bound.
Fig. 3. Block Diagram with attention model
To start with automatic image caption generation, image annotation was studied from Image Annotation via deep neural network  which proposes a novel framework of multimodal deep learning where the convolutional neural networks (CNN) with unlabeled data is utilized to pre-train the multimodal deep neural network to learn intermediate representations and provide a good initialization for the network then use backpropagation to optimize the distance metric functions on individual modality. This was followed by Automatic image annotation using DL representation  in which the last layer of CaffeNet of the CNN based model is replaced with a projection layer to perform regression and the resulting network is trained for mapping images to semantically meaningful word embedding vectors. Advantage of this modelling is: firstly, it does not require dozens of handcrafted features and secondly, the approach is simpler to formulate than any other generative or discriminative models. A single network is created for generating captions of images in Show and Tell: A Neural Image Caption Generator . In this network, deep convolutional network is used for image classification and sentence generation is done by a powerful Recurrent Neural Network which is trained with the visual input so that RNN can keep track of the objects explained by the text. A different approach to caption generation is incorporated in Show, Attend and Tell: Neural Image Caption Generation with Visual Attention  where, a form of attention, hard
attention mechanism and soft attention mechanism are described. A deterministic soft attention mechanism is employed by standard back-propagation methods and a stochastic hard attention mechanism by maximizing an
In this paper we propose a transfer learning approach to generate automated captions for any given image. In this model the encoder used is pre-trained VGG16 model. This model makes use of a recurrent neural network which encodes the variable length input into a fixed dimensional vector and uses this representation to decode it to the desired output sentence. The vector containing the output of the fully connected layer in VGG16 is connected to GRU units is called thought vector.
In the encoder of the system, pre-trained models VGG 16, RESNET and Inception are used to compare between the results obtained from each of them. The encoder is used to extract thought vector of the image which describes the contents of the image.
Pretrained VGG model: The softmax layer of the VGG model is striped to avoid classification of the image and instead the information about the entire image is obtained. The hidden layers of VGG model consists of 2 convolutional layers followed by max pooling layer to reduce the size by half. This architecture is repeated 3 times followed by flatten layer to get a one-dimensional output which is fed to fully connected layers. The output of second fully connected layer is taken as the initial state of GRU layers in the decoder after it is downsized by the dense map layer. The summary of VGG model is shown in Figure 5.
The decoder consists of Tokenizer, embedding layer, GRU layers and dense layer. Each of the captions are first prepended and appended by start and end marker respectively. Tokenizer layer is used to convert the first predefined number of unique words into integer tokens. Once the tokens are assigned, the embedding layer converts integer-tokens into vectors of 128 floating-point number since the RNN network works on vectors and not integers. The sequence vectors are padded to ensure that all the sequence vectors are of same length which is equal to the length of the maximum sequence. The GRU unit comprises of three gates: forget gate, output gate, input gate. The gates are defined as:
Fig. 4. GRU architectures
ht = zt ht-1 + (1 – zt) ht zt = ( W(z)xt + U(z)ht-1
rt = ( W(r)t + U(r)ht-1 )
ht = tanh( Wxt + rt U ht-1 )
The output of the GRU network is further given to the dense layer to convert the sequences into integer tokens which generates output after passing through token_to_word instance of tokenizer object. The steps per epoch is calculated as:
steps per epoch
total number of caption sin the training data set
= Batch size
Loss is computed using sparse softmax cross entropy with logits which measures the probability error in discrete classification tasks in which the classes are mutually exclusive (each entry is in exactly one class). The optimizer used is RMSprop instead of Adam optimizer for better results. The activation used in the last dense layer is linear activation.
B. Image Captioning With Attention Model
In the previous block diagram (Figure 2), as explained, the approach was to encode image into vector representation using CNN encoder and decode into word vectors signifying captions using RNN. The problem with this approach is that while generating a single caption, GRU every time looks at the entire image vector representation and this seems inefficient as different words in a caption are generated by looking at specific parts of the image.
As a solution to this problem,  introduced the concept of attention into image captioning. Figure 3 shows the block diagram of Image captioning with Attention. The idea was to view the image from the perceptive of captioner to decide what is important and what is not important when it comes to captioning an image or in other words, trying to decide what details are worth paying attention to. The paper discussed two types of attention: 1. Hard attention 2. Soft
attention.Attention mechanism starts by first creating N different non overlapping sub regions of the feature vector representation which is the output of CNN encoder. Attention unit takes all the sub regions (say Yi, i=1,2, N) and context (C) as input and outputs weighted arithmetic means of these regions. Context C is the collection of recent outputs of RNN.
4 Automated Audio Captioning
Automated audio captioning (AAC) is the task of general audio content description using free text. It is an inter-modal translation task (not speech-to-text), where a system accepts as an input an audio signal and outputs the textual description (i.e. the caption) of that signal. AAC methods can model concepts (e.g. "muffled sound"), physical properties of objects and environment (e.g. "the sound of a big car", "people talking in a small and empty room"), and high level knowledge ("a clock rings three times"). This modeling can be used in various applications, ranging from automatic content description to intelligent and content oriented machine-to-machine interaction.
Figure 5. An example of an automated audio captioning system and process.
The task of AAC is a continuation of the AAC task from DCASE2020. Compared to DCASE2020, this year the task of AAC will allow the usage of any external data and/or pre-trained models. For example, now participants are allowed to use other datasets for AAC or even datasets for sound event detection/tagging, acoustic scene classification, or datasets from any other task that might deemed fit. Additionally, participants can now use pre-trained models, like (but not limited to) Word2Vec, BERT, and YAMNet, wherever they want in their model. Please see below for some recommendations for datasets and pre-tuned models. Finally, this year Clotho dataset will be augmented by around 40% more data, providing a publicly available validation split and extra data in the training split, which participants can use in order to develop their methods. The new version of Clotho will be referred to as Clotho v2, it is expected to be available late March, and the exact numbers for Clotho v2 (e.g. exact amount of words and exact amount of audio samples) will be known upon release
RESULTS AND ANALYSIS
A. Results Of Image Captioning Generation
Using VGG16 model as an encoder with 16 hidden layers and GRU network (using 3 GRU layers) as decoder with number of epochs set to 20 for optimum performance taking 118287 images from MS-COCO dataset as training dataset and vocabulary size of 10000 unique words, Figure 6 shows prediction for an image in validation set. Figure 8 shows the image and the predicted caption for an image from testing dataset.
Fig 6.result 1
Fig 7.result 2
Fig 8.result 3
Fig 9.result 4
Thus, majority of images in validation dataset . The table 1 summaries the observation and analysis of the Result..
Nature of predicted sentence
0.8 – 0.9
Automatically generates image caption.
Gives summary about the image
It does not require dozens of Handcrafted features
Accuracy is not upto the mark
Training may take a very long time
Requires lot of data
We have presented an end-to-end neural network system that can automatically view an image and generate a reasonable description in plain English. It is based on a convolution neural network that encodes an image into a compact representation, followed by a recurrent neural network that generates a corresponding sentence. The model is trained to maximize the likelihood of the sentence given the image. We also saw the effect of the encoder-decoder approach combined with attention and made analysis.
The following conclusions were drawn:
The number of epochs required for the same dataset varies for different models.
As the network becomes deeper, the number of epochs for Image captioning problem becomes less.
There is a trade-off between time required for execution and the number of hidden layers.
The measurement of average value of different metrics on the same dataset with different models and different number of epochs was done.
The precision with which different metrics evaluate performance of system as compared to the human generated captions was judged.
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