Image Caption Generation Using Deep Learning

A novel framework for automatic caption and audio generation

Recently, the progress on image understanding and AIC (Automatic Image Captioning) has attracted lots of researchers to make use of AI (Artificial Intelligence) models to assist the blind people. AIC integrates the principle of both computer vision and NLP (Natural Language Processing) to generate automatic language descriptions in relation to the image observed. This work presents a new assistive technology based on deep learning which helps the blind people to distinguish the food items in online grocery shopping. The proposed AIC model involves the following steps such as Data Collection, Non-captioned image selection, Extraction of appearance, texture features and Generation of automatic image captions. Initially, the data is collected from two public sources and the selection of non-captioned images are done using the ARO (Adaptive Rain Optimization). Next, the appearance feature is extracted using SDM (Spatial Derivative and Multi-scale) approach and WPLBP (Weighted Patch Local Binary Pattern) is used in the extraction of texture features. Finally, the captions are automatically generated using ECANN (Extended Convolutional Atom Neural Network). ECANN model combines the CNN (Convolutional Neural Network) and LSTM (Long Short-Term Memory) architectures to perform the caption reusable system to select the most accurate caption. The loss in the ECANN architecture is minimized using AAS (Adaptive Atom Search) Optimization algorithm. The implementation tool used is PYTHON and the dataset used for the analysis are Grocery datasets (Freiburg Groceries and Grocery Store Dataset). The proposed ECANN model acquired accuracy (99.46%) on Grocery Store Dataset and (99.32%) accuracy on Freiburg Groceries dataset. Thus, the performance of the proposed ECANN model is compared with other existing models to verify the supremacy of the proposed work over the other existing works.

Automatic caption generation from images has emerged as a fundamental and challenging problem at the intersection of computer vision and natural language processing. This paper presents a comprehensive survey of the techniques, methodologies, and advancements in the field of automatic caption generation from images. The primary objective is to provide an extensive review of the state-of-the-art models, evaluation metrics, datasets, and applications associated with this domain. The survey begins by elucidating the underlying principles of image feature extraction and caption generation. Various neural network architectures, including Convolutional Neural Networks (CNNs) and recurrent models such as Long Short-Term Memory (LSTM) networks, are discussed in detail. Additionally, the paper explores the integration of attention mechanisms and reinforcement learning strategies to enhance the quality and relevance of generated captions. A thorough examination of evaluation metrics, encompassing both automated and human-centric approaches, is presented to evaluate the generated captions quantitatively and qualitatively. The survey also highlights prominent datasets that have significantly contributed to the advancement of research in this field, facilitating a deeper understanding of challenges and trends. Furthermore, the paper discusses practical applications and real-world use cases where automatic caption generation plays a pivotal role, including accessibility, multimedia indexing, and assistive technologies. The discussion concludes by outlining open challenges and future directions, aiming to inspire further research and innovation in automatic caption generation from images. The aim of this paper is to examine and contrast diverse end-to-end learning frameworks for image captioning, employing established evaluation metrics to comprehend their applicability across different research domains. In addition to the comparative analysis, the paper addresses future challenges in this domain.

Image Captioning is a task where each image must be understood properly and are able generate suitable caption with proper grammatical structure.Here it is a hybrid system which uses multilayer CNN (Convolutional Neural Network) for generating keywords which narrates given input images and Long Short Term Memory(LSTM) for precisely constructing the significant captions utilizing the obtained words .Convolution Neural Network (CNN) proven to be so effective that there is a way to get to any kind of estimating problem that includes image data as input. LSTM was developed to avoid the poor predictive problem which occurred while using traditional approaches. We used an encoder-decoder based model that is capable of generating grammatically correct captions for images. This model makes use of VGG16(Visual Geometry Group) as an encoder and LSTM as a decoder. The model will be trained like when an image is given model produces captions that almost describe the image. The efficiency is demonstrated for the given model using Flickr8K data sets which contains 8000 images and captions for each image but we use CNN and LSTM to capture dependencies and tell both the spatial relationships of images and contextual information of captions and generate contextually relevant captions. Keywords—CNN(Convolutional Neural Network),LSTM(Long Short Term Memory),VGG16(Visual Geometry Group),Deep Learning,Encoder-Decoder.

Aided by recent advances in Deep Learning, Image Caption Generation has seen tremendous progress over the last few years. Most methods use transfer learning to extract visual information, in the form of image features, with the help of pre-trained Convolutional Neural Network models followed by transformation of the visual information using a Caption Generator module to generate the output sentences. Different methods have used different Convolutional Neural Network Architectures and, to the best of our knowledge, there is no systematic study which compares the relative efficacy of different Convolutional Neural Network architectures for extracting the visual information. In this work, we have evaluated 17 different Convolutional Neural Networks on two popular Image Caption Generation frameworks: the first based on Neural Image Caption (NIC) generation model and the second based on Soft-Attention framework. We observe that model complexity of Convolutional Neural Network, as measured by number of parameters, and the accuracy of the model on Object Recognition task does not necessarily co-relate with its efficacy on feature extraction for Image Caption Generation task. We release the code at https://github.com/iamsulabh/cnn variants.

This project introduces a deep learning based image caption generator, merging computer vision and natural language processing. Leveraging pre-trained convolutional neural networks (CNNs) like InceptionV3 and recurrent neural networks (RNNs), the model extracts image features and generates coherent captions. Using datasets like MS COCO, the system is trained to map image features to corresponding captions. The model architecture incorporates beddings, LSTM layers, and dense layers, optimizing parameters with categorical cross-entropy loss during training. There sulting model can generate meaningful captions for new images, showcasing the synergy between visual understanding and language generation in the realm of multimedia applications. The proposed image caption generator shows the fusion of computer vision and natural language processing capabilities. Using deep learning techniques, specifically pre-trained CNNs andRNNs, allows for the creation of a model capable of generating contextually relevant captions for a diverse range of images.This work contributes to the evolving landscape of multimedia applications, showcasing the potential of deep learning in understanding and generating human like descriptions of visual content.

Image captioning consists of the description of images by identifying the main objects of an image, the features of the objects, and their associations. The effective system should also produce syntactically and semantically correct sentences. Deep learning methods can be effective in addressing the complications involved in this task. The article presents an advanced deep learning architecture of image captioning that enable the implication of three advanced technologies i.e., machine vision, machine translation and transfer learning. The state-of-the-art CNN architecture have been utilized to perform this task i.e., DenseNet201 model. DenseNet201 is a convolutional neural network (CNN) which converts the image data into a feature vector. After this CNN, a recurrent neural network (RNN) is exploited to encode the images using this vector. The coded text is then passed through another RNN, which is known as Long Short-Term Memory (LSTM) networks where the feature vector is decoded to produce a sequence of words which finally form the image descriptions. The Flickr8k dataset is used to test the effectiveness of the proposed model, and the performance of the model is measured with the help of the BLEU metric, which then gives a quantitative evaluation of the potential of the model.

Image captioning is a deep learning technique that intends to create and generate textual descriptions or captions for images. It integrates computer vision and natural language processing (NLP) to comprehend the visual content of an image and generate human-like descriptions. Deep learning (DL) based image captioning models can be trained on large-scale datasets, allowing them to generalize various types of images and generate captions that apply to a wide range of visual scenarios. By combining computer vision and natural language processing, DL-enabled image captioning models can understand both visual and textual information, which enables them to generate captions that not only describe the visual content but also incorporate contextual and semantic information. This study develops an Automated Image Captioning using Sparrow Search Algorithm with Improved Deep Learning (AIC-SSAIDL) technique. The major intention of the AIC-SSAIDL technique lies in the automated generation of textual captions for the input images. To accomplish this, the AIC-SSAIDL technique utilizes the MobileNetv2 model to generate feature descriptors of the input images and its hyperparameter tuning process takes place using SSA. For the image captioning process, the AIC-SSAIDL technique utilizes an attention mechanism with long short-term memory (AM-LSTM) network. Finally, the hyperparameter selection of the AM-LSTM model is performed by the fruit fly optimization (FFO) algorithm. A wide range of experiments has been conducted on benchmark data to depict the better performance of the AIC-SSAIDL method. The comprehensive result analysis highlighted the enhanced captioning results of the AIC-SSAIDL method with maximum CIDEr of 46.12, 61.89, and 137.45 on Flickr8k, Flickr30k, and MSCOCO datasets, respectively.

Automatic image caption generation is a challenging AI problem since it requires utilization of several techniques from different computer science domains such as computer vision and natural language processing. Deep learning techniques have demonstrated outstanding results in many different applications. However, data augmentation in deep learning, which replicates the amount and the variety of training data available for learning models without the burden of collecting new data, is a promising field in machine learning. Generating textual description for a given image is a challenging task for computers. Nowadays, deep learning performs a significant role in the manipulation of visual data with the help of Convolutional Neural Networks (CNN). In this study, CNNs are employed to train prediction models which will help in automatic image caption generation. The proposed method utilizes the concept of data augmentation to overcome the fuzziness of well-known image caption generation models. Flickr8k dataset is used in the experimental work of this study and the BLEU score is applied to evaluate the reliability of the proposed method. The results clearly show the stability of the outcomes generated through the proposed method when compared to others.

Image captioning refers to the automated process of generating a descriptive sentence that conveys the content of a given image. The developed model receives an image as input and produces an English sentence that accurately represents what is depicted. This area has drawn considerable attention in recent years, particularly in the realm of cognitive computing, due to its reliance on both computer vision and natural language processing techniques. The system utilizes a Convolutional Neural Network (CNN) to analyze and extract visual features from the image, which are then passed to a Long Short- Term Memory (LSTM) network responsible for constructing the descriptive sentence. The CNN functions as the encoder, while the LSTM acts as the decoder. Following caption generation, the model's performance is evaluated to ensure the quality and relevance of the output. This enables the generation of meaningful, human-readable descriptions for various images

Classification of EEG signals is a cornerstone of building the motor-imagery (MI) based Brain-computer interface (BCI) systems. EEG signals differ from one subject to another and even for the same subject among different trials, and this is why designing a general classification model is still debated. Deep learning is dominant in so many fields like computer vision and natural language processing but it is still under investigation for EEG signals classification. We followed a new trend in EEG signals classification in which these signals are transformed into images, and so classifying such signals become an image classification problem where Deep learning can work well. The Physionet dataset for EEG motor movement/imagery tasks was used which consists of 109 subjects and the motor imagery EEG signals for three frequency bands (Delta [0.5-4 Hz], Mu [8–13 Hz], and Beta [13–30 Hz]) was transformed into 3-channel images (one channel for each band) using the Azimuthal equidistant projection and Clough-Tocher algorithm for interpolation. These 2-D images represent the input data to our model which consists of Deep Convolutional Neural Network (DCNN) to extract the spatial and frequency features followed by Long Short Term Memory (LSTM) to extract temporal features and then finally to be classified into 5 different classes (4 motor imagery tasks and one rest). Our results were promising (70.64% average accuracy) and 5% better than the results of Support Vector Machine (SVM) method over the same dataset. We noticed that taking Delta band into account increases the classification accuracy by 2.51%.

The project aims to develop an advanced Image Caption Generator using deep learning techniques and computer vision algorithms. In an era of increasing visual content on the internet, the ability to automatically generate descriptive captions for images has become crucial for enhancing accessibility and user experience. This project leverages state-of the-art deep neural networks, specifically Convolutional Neural Networks (CNNs) for image feature extraction and Recurrent Neural Networks (RNNs) for generating coherent and contextually relevant captions. The system takes an image as input and employs a pre-trained CNN to extract high level features, creating a rich representation of the visual content. Subsequently, an RNN-based sequence-to-sequence model processes these features to generate natural language captions. To improve the quality and fluency of captions, the model incorporates attention mechanisms, allowing it to focus on different parts of the image while generating each word. The outcome of this project has broad applications in fields such as image indexing, content retrieval, and accessibility, making digital visual content more understandable and engaging for a wide range of users. Additionally, the project contributes to the advancement of deep learning techniques in computer vision and natural language processing, pushing the boundaries of Al capabilities in understanding and describing visual information. KeyWords Image Captioning,Convolutional Neural Network (CNN),Recurrent Neural Network (RNN),Long Short-Term Memory (LSTM),Encoder-Decoder Architecture,Feature Extraction,Image Features,Sequence Generation,Natural Language Processing (NLP)

The rapid advancement in image captioning has been a pivotal area of research, aiming to mimic human-like understanding of visual content. This paper presents an innovative approach that integrates attention mechanisms and object features into an image captioning model. Leveraging the Flickr8k dataset, this research explores the fusion of these components to enhance image comprehension and caption generation. Furthermore, the study showcases the implementation of this model in a user-friendly application using FASTAPI and ReactJS, offering text-to-speech translation in multiple languages. The findings underscore the efficacy of this approach in advancing image captioning technology. This tutorial outlines the construction of an image caption generator, employing Convolutional Neural Network (CNN) for image feature extraction and Long Short-Term Memory Network (LSTM) for Natural Language Processing (NLP). Keywords—Convolutional Neural Networks, Long Short Term Memory, Attention Mechanism, Transformer Architecture, Vision Transformers, Transfer Learning, Multimodal fusion, Deep Learning Models, Pre-Trained Models, Image Processing Techniques

With the rapid development of Deep learning, AI along with Computer Vision and Natural Language processing Image caption has become an interesting and complex task. Image caption generation is the process of generating textual description of the given image and it is a challenging task because it consists of apprehension of objects. If the machine will be programmed to accurately describe an image or environment like human vision, it will be highly beneficial for robotic vision, business and many more. In order to generate an effective description of the image, the machine needs to detect, recognize objects as well as understand the scene type or location, object properties, their relationships and their interactions with each other. In this paper, we focus on advanced image captioning techniques such as CNN (Convolutional Neural Network)-LSTM(Long Short Term Memory) to generate meaningful captions. and the advantages and limitations of each method are discussed.

Image Captioning is one of the emerging topics of research in the field of AI. It uses a combination of Computer Vision (CV) and Natural Language Processing (NLP) to derive features from the image, use this information to identify objects, actions, their relationships, and generate a description for the image. It is most important concept in artificial intelligence applied in the fields like aid to the blind, self-driving cars, and many more. This paper we demonstrates a concise state of art image captioning and its method for caption generation using deep learning concepts. We also determine the approach for image caption generation using Convolutional Neural Network (CNN) and Generative Adversarial Network (GAN) model in deep learning framework. Using this approach system intelligent enough to create sentences for images. It uses the encoder-decoder architecture, where CNN is used for image vector generation and LSTM is used for the generation of a logical sentence using the NLP concepts. Finally, we evaluate the proposed system experimental analysis with numerous existing systems and show the effeteness of system.