scholarly journals Image Caption Generator

2021 ◽  
Vol 10 (3) ◽  
pp. 87-92
Author(s):  
Megha J Panicker ◽  
Vikas Upadhayay ◽  
Gunjan Sethi ◽  
Vrinda Mathur
Keyword(s):  
Computer Vision ◽  
Language Processing ◽  
Network Models ◽  
Image Captioning ◽  
Neural Network Models ◽  
Visually Impaired People ◽  
Video Frames ◽  
Modern Era ◽  
Deep Learning Model ◽  
Image Caption

In the modern era, image captioning has become one of the most widely required tools. Moreover, there are inbuilt applications that generate and provide a caption for a certain image, all these things are done with the help of deep neural network models. The process of generating a description of an image is called image captioning. It requires recognizing the important objects, their attributes, and the relationships among the objects in an image. It generates syntactically and semantically correct sentences.In this paper, we present a deep learning model to describe images and generate captions using computer vision and machine translation. This paper aims to detect different objects found in an image, recognize the relationships between those objects and generate captions. The dataset used is Flickr8k and the programming language used was Python3, and an ML technique called Transfer Learning will be implemented with the help of the Xception model, to demonstrate the proposed experiment. This paper will also elaborate on the functions and structure of the various Neural networks involved. Generating image captions is an important aspect of Computer Vision and Natural language processing. Image caption generators can find applications in Image segmentation as used by Facebook and Google Photos, and even more so, its use can be extended to video frames. They will easily automate the job of a person who has to interpret images. Not to mention it has immense scope in helping visually impaired people.

A Hindi Image Caption Generation Framework Using Deep Learning

2021 ◽  
Vol 20 (2) ◽  
pp. 1-19
Author(s):  
Santosh Kumar Mishra ◽  
Rijul Dhir ◽  
Sriparna Saha ◽  
Pushpak Bhattacharyya
Keyword(s):  
Computer Vision ◽  
Natural Language Processing ◽  
Natural Language ◽  
Language Processing ◽  
English Language ◽  
Image Captioning ◽  
Textual Description ◽  
Proposed Model ◽  
Hindi Language ◽  
The Given

Image captioning is the process of generating a textual description of an image that aims to describe the salient parts of the given image. It is an important problem, as it involves computer vision and natural language processing, where computer vision is used for understanding images, and natural language processing is used for language modeling. A lot of works have been done for image captioning for the English language. In this article, we have developed a model for image captioning in the Hindi language. Hindi is the official language of India, and it is the fourth most spoken language in the world, spoken in India and South Asia. To the best of our knowledge, this is the first attempt to generate image captions in the Hindi language. A dataset is manually created by translating well known MSCOCO dataset from English to Hindi. Finally, different types of attention-based architectures are developed for image captioning in the Hindi language. These attention mechanisms are new for the Hindi language, as those have never been used for the Hindi language. The obtained results of the proposed model are compared with several baselines in terms of BLEU scores, and the results show that our model performs better than others. Manual evaluation of the obtained captions in terms of adequacy and fluency also reveals the effectiveness of our proposed approach. Availability of resources : The codes of the article are available at https://github.com/santosh1821cs03/Image_Captioning_Hindi_Language ; The dataset will be made available: http://www.iitp.ac.in/∼ai-nlp-ml/resources.html .

scholarly journals Analysis Methods in Neural Language Processing: A Survey

2019 ◽  
Vol 7 ◽  
pp. 49-72 ◽  
Author(s):  
Yonatan Belinkov ◽  
James Glass
Keyword(s):  
Language Processing ◽  
Network Models ◽  
Research Trends ◽  
Neural Network Models ◽  
Fine Grained ◽  
Survey Paper ◽  
Review Analysis ◽  
Analysis Methods ◽  
New Models ◽  
Future Work

The field of natural language processing has seen impressive progress in recent years, with neural network models replacing many of the traditional systems. A plethora of new models have been proposed, many of which are thought to be opaque compared to their feature-rich counterparts. This has led researchers to analyze, interpret, and evaluate neural networks in novel and more fine-grained ways. In this survey paper, we review analysis methods in neural language processing, categorize them according to prominent research trends, highlight existing limitations, and point to potential directions for future work.

Improved Framework using Rider Optimization Algorithm for Precise Image Caption Generation

2021 ◽  
pp. 2250021
Author(s):  
Chaitrali Prasanna Chaudhari ◽  
Satish Devane
Keyword(s):  
Language Processing ◽  
Optimization Algorithm ◽  
Short Term Memory ◽  
Activation Function ◽  
Input Image ◽  
Image Captioning ◽  
Word Generation ◽  
Adopted Model ◽  
Conventional Models ◽  
Image Caption

“Image Captioning is the process of generating a textual description of an image”. It deploys both computer vision and natural language processing for caption generation. However, the majority of the image captioning systems offer unclear depictions regarding the objects like “man”, “woman”, “group of people”, “building”, etc. Hence, this paper intends to develop an intelligent-based image captioning model. The adopted model comprises of few steps like word generation, sentence formation, and caption generation. Initially, the input image is subjected to the Deep learning classifier called Convolutional Neural Network (CNN). Since the classifier is already trained in the relevant words that are related to all images, it can easily classify the associated words of the given image. Further, a set of sentences is formed with the generated words using Long-Short Term Memory (LSTM) model. The likelihood of the formed sentences is computed using the Maximum Likelihood (ML) function, and the sentences with higher probability are taken, which is further used for generating the visual representation of the scene in terms of image caption. As a major novelty, this paper aims to enhance the performance of CNN by optimally tuning its weight and activation function. This paper introduces a new enhanced optimization algorithm Rider with Randomized Bypass and Over-taker update (RR-BOU) for this optimal selection. In the proposed RR-BOU is the enhanced version of the Rider Optimization Algorithm (ROA). Finally, the performance of the proposed captioning model is compared over other conventional models with respect to statistical analysis.

scholarly journals Unified Framework for Vision Inference on the Edge

2020 ◽  
Author(s):  
Vysakh S Mohan
Keyword(s):  
Computer Vision ◽  
Mobile Robotics ◽  
Low Cost ◽  
Network Models ◽  
Ease Of Use ◽  
Use Cases ◽  
Unified Framework ◽  
Neural Network Models ◽  
Inference Models ◽  
Small Form Factor

Edge processing for computer vision systems enable incorporating visual intelligence to mobile robotics platforms. Demand for low power, low cost and small form factor devices are on the rise.This work proposes a unified platform to generate deep learning models compatible on edge devices from Intel, NVIDIA and XaLogic. The platform enables users to create custom data annotations,train neural networks and generate edge compatible inference models. As a testimony to the tools ease of use and flexibility, we explore two use cases — vision powered prosthetic hand and drone vision. Neural network models for these use cases will be built using the proposed pipeline and will be open-sourced. Online and offline versions of the tool and corresponding inference modules for edge devices will also be made public for users to create custom computer vision use cases.

scholarly journals Middle-Level Attribute-Based Language Retouching for Image Caption Generation

2018 ◽  
Vol 8 (10) ◽  
pp. 1850 ◽  
Author(s):  
Zhibin Guan ◽  
Kang Liu ◽  
Yan Ma ◽  
Xu Qian ◽  
Tongkai Ji
Keyword(s):  
Natural Language ◽  
Language Processing ◽  
Middle Level ◽  
Generation Model ◽  
Image Description ◽  
Image Captioning ◽  
Benchmark Datasets ◽  
Intermediate Image ◽  
Image Caption Generation ◽  
Image Caption

Image caption generation is attractive research which focuses on generating natural language sentences to describe the visual content of a given image. It is an interdisciplinary subject combining computer vision (CV) and natural language processing (NLP). The existing image captioning methods are mainly focused on generating the final image caption directly, which may lose significant identification information of objects contained in the raw image. Therefore, we propose a new middle-level attribute-based language retouching (MLALR) method to solve this problem. Our proposed MLALR method uses the middle-level attributes predicted from the object regions to retouch the intermediate image description, which is generated by our language generation model. The advantage of our MLALR method is that it can correct descriptive errors in the intermediate image description and make the final image caption more accurate. Moreover, evaluation using benchmark datasets—MSCOCO, Flickr8K, and Flickr30K—validated the impressive performance of our MLALR method with evaluation metrics—BLEU, METEOR, ROUGE-L, CIDEr, and SPICE.

scholarly journals Comparison of rule-based and neural network models for negation detection in radiology reports

2020 ◽  
pp. 1-22 ◽  
Author(s):  
D. Sykes ◽  
A. Grivas ◽  
C. Grover ◽  
R. Tobin ◽  
C. Sudlow ◽  
...  
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Language Processing ◽  
Network Models ◽  
Neural Network Models ◽  
Test Set ◽  
Rule Based ◽  
Radiology Reports ◽  
The Neural Network ◽  
Negation Detection

Abstract Using natural language processing, it is possible to extract structured information from raw text in the electronic health record (EHR) at reasonably high accuracy. However, the accurate distinction between negated and non-negated mentions of clinical terms remains a challenge. EHR text includes cases where diseases are stated not to be present or only hypothesised, meaning a disease can be mentioned in a report when it is not being reported as present. This makes tasks such as document classification and summarisation more difficult. We have developed the rule-based EdIE-R-Neg, part of an existing text mining pipeline called EdIE-R (Edinburgh Information Extraction for Radiology reports), developed to process brain imaging reports, (https://www.ltg.ed.ac.uk/software/edie-r/) and two machine learning approaches; one using a bidirectional long short-term memory network and another using a feedforward neural network. These were developed on data from the Edinburgh Stroke Study (ESS) and tested on data from routine reports from NHS Tayside (Tayside). Both datasets consist of written reports from medical scans. These models are compared with two existing rule-based models: pyConText (Harkema et al. 2009. Journal of Biomedical Informatics42(5), 839–851), a python implementation of a generalisation of NegEx, and NegBio (Peng et al. 2017. NegBio: A high-performance tool for negation and uncertainty detection in radiology reports. arXiv e-prints, p. arXiv:1712.05898), which identifies negation scopes through patterns applied to a syntactic representation of the sentence. On both the test set of the dataset from which our models were developed, as well as the largely similar Tayside test set, the neural network models and our custom-built rule-based system outperformed the existing methods. EdIE-R-Neg scored highest on F1 score, particularly on the test set of the Tayside dataset, from which no development data were used in these experiments, showing the power of custom-built rule-based systems for negation detection on datasets of this size. The performance gap of the machine learning models to EdIE-R-Neg on the Tayside test set was reduced through adding development Tayside data into the ESS training set, demonstrating the adaptability of the neural network models.

scholarly journals Unified Framework for Vision Inference on the Edge

2020 ◽  
Author(s):  
Vysakh S Mohan
Keyword(s):  
Computer Vision ◽  
Mobile Robotics ◽  
Low Cost ◽  
Network Models ◽  
Ease Of Use ◽  
Use Cases ◽  
Unified Framework ◽  
Neural Network Models ◽  
Inference Models ◽  
Small Form Factor

Edge processing for computer vision systems enable incorporating visual intelligence to mobile robotics platforms. Demand for low power, low cost and small form factor devices are on the rise.This work proposes a unified platform to generate deep learning models compatible on edge devices from Intel, NVIDIA and XaLogic. The platform enables users to create custom data annotations,train neural networks and generate edge compatible inference models. As a testimony to the tools ease of use and flexibility, we explore two use cases — vision powered prosthetic hand and drone vision. Neural network models for these use cases will be built using the proposed pipeline and will be open-sourced. Online and offline versions of the tool and corresponding inference modules for edge devices will also be made public for users to create custom computer vision use cases.

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