From Pixels to Phrases: Enhancing Image Captioning with LSTM Model
Author(s):
Pulkit Dwivedi
Keywords:
Image captioning, Long Short-Term Memory (LSTM), Convolutional Neural Networks (CNNs), Natural lan- guage processing (NLP), Deep learning
Abstract
Generating natural language captions for images is an important task that requires understanding and identi- fying the objects within an image. However, the effectiveness of image caption generation has not been thoroughly proven. To address this gap, we propose a novel approach that com- bines Convolutional Neural Networks (CNNs) and Long Short- Term Memory (LSTM) models to generate image captions. Our approach comprises two sub-models: an Object Identification model and a Localization model that extract information about objects and their spatial relationships from images. We then use LSTM models to process the extracted text data, encoding the text input sequence as a fixed-length output vector. Finally, we integrate the image vector outputs and the corresponding descriptions to train the image caption generator model. We compare the performance of our LSTM-based model with other dense models, including VGG-16 and Transformer-based models, using the Flickr8k dataset. Our experimental results demonstrate that our LSTM-based approach outperforms previous VGG and Transformer-based models, as well as state-of-the-art image captioning models. By integrating image and text data using LSTM models, our approach provides a new benchmark for image caption generation, advancing the state-of-the-art in this critical area of research.
Article Details
Unique Paper ID: 159666

Publication Volume & Issue: Volume 9, Issue 12

Page(s): 486 - 492
Article Preview & Download


Share This Article

Conference Alert

NCSST-2023

AICTE Sponsored National Conference on Smart Systems and Technologies

Last Date: 25th November 2023

SWEC- Management

LATEST INNOVATION’S AND FUTURE TRENDS IN MANAGEMENT

Last Date: 7th November 2023

Latest Publication

Go To Issue



Call For Paper

Volume 10 Issue 1

Last Date for paper submitting for March Issue is 25 June 2023

About Us

IJIRT.org enables door in research by providing high quality research articles in open access market.

Send us any query related to your research on editor@ijirt.org

Social Media

Google Verified Reviews

Contact Details

Telephone:6351679790
Email: editor@ijirt.org
Website: ijirt.org

Policies

Important Links

Browse Rsearch papers