IMAGE CAPTION GENERATION USING LSTMS AND CONVOLUTIONAL NEURAL NETWORKS

IMAGE CAPTION GENERATION USING LSTMS AND CONVOLUTIONAL NEURAL NETWORKS

Registration ID: IJNRD_205981

Published ID: IJNRD2309391

DOI: http://doi.one/10.1729/Journal.36336

D Kanishya Gayathri , L Prinslin

Image Captioning, Convolutional Neural Networks, LSTMs, Deep Learning, Computer Vision, Natural Language Processing, MSCOCO Dataset, Data Preprocessing, Model Architecture, Training, Evaluation Metrics, Encoder-Decoder, Teacher Forcing, Cross-Entropy Loss, Metric-Based Evaluation, BLEU, METEOR, CIDEr, ROUGE, Multimodal AI, Visual Understanding, Text Generation, Image Description, Machine Learning, Deep Neural Networks.

Image caption generation is a captivating intersection of computer vision and natural language processing, with applications spanning assistive technology, content retrieval, and human-computer interaction. In this project, we delve into the fusion of Convolutional Neural Networks (CNNs) and Long Short-Term Memory networks (LSTMs) to address this intriguing challenge. Our project centres on the holistic development of an image captioning system. We kick-started the journey with meticulous data collection and pre-processing, leveraging established datasets like MSCOCO. The images underwent resizing, normalization, and feature extraction using a pre-trained CNN, serving as our image encoder. The nucleus of our innovation resides in the model architecture. We meticulously designed a two-tiered structure, comprising a CNN as the image encoder and an LSTM as the text decoder. The CNN's role is to extract salient image features, while the LSTM excels at generating coherent and contextually relevant captions, guided by a cross-entropy loss function during training. Teacher forcing further stabilizes our model's convergence. Our system underwent rigorous evaluation employing a battery of metrics, including BLEU, METEOR, CIDEr, and ROUGE, benchmarking the generated captions against human-annotated references. This quantitative analysis provides an in-depth perspective on the system's performance and underscores areas for enhancement. As we conclude this report, we reflect on the challenges encountered throughout our project's lifecycle and propose avenues for future research in the captivating realm of image caption generation. Our aspiration is to refine and enhance our system, enabling it to generate not just accurate but also contextually rich captions—an endeavor that advances the frontiers of multimodal AI applications. In summary, this project showcases the symbiotic relationship between computer vision and natural language processing, underscoring the potential of CNN-LSTM architectures in the captivating domain of image captioning. Our work contributes to the burgeoning field of multimodal AI, fostering innovative applications across diverse domains.

Published Paper   E-Certificate

"IMAGE CAPTION GENERATION USING LSTMS AND CONVOLUTIONAL NEURAL NETWORKS", IJNRD - INTERNATIONAL JOURNAL OF NOVEL RESEARCH AND DEVELOPMENT (www.IJNRD.org), ISSN:2456-4184, Vol.8, Issue 9, page no.d775-d781, September-2023, Available :https://ijnrd.org/papers/IJNRD2309391.pdf

Volume 8 Issue 9, September-2023

Pages : d775-d781

Paper Reg. ID: IJNRD_205981

Published Paper Id: IJNRD2309391

Downloads: 000121132

Research Area: Engineering

Country: Chennai, Tamilnadu, India

Published Paper PDF: https://ijnrd.org/papers/IJNRD2309391.pdf

Published Paper URL: https://ijnrd.org/viewpaperforall?paper=IJNRD2309391

DOI: http://doi.one/10.1729/Journal.36336

Journal Name: INTERNATIONAL JOURNAL OF NOVEL RESEARCH AND DEVELOPMENT(IJNRD)

ISSN: 2456-4184 | IMPACT FACTOR: 8.76 Calculated By Google Scholar | ESTD YEAR: 2016

An International Scholarly Open Access Journal, Peer-Reviewed, Refereed Journal Impact Factor 8.76 Calculate by Google Scholar and Semantic Scholar | AI-Powered Research Tool, Multidisciplinary, Monthly, Multilanguage Journal Indexing in All Major Database & Metadata, Citation Generator

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This work is licensed under a Creative Commons Attribution 4.0 International License and The Open Definition

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