Paper Title
IMAGE CAPTION GENERATION USING LSTMS AND CONVOLUTIONAL NEURAL NETWORKS
Article Identifiers
Registration ID: IJNRD_205981
Published ID: IJNRD2309391
: http://doi.one/10.1729/Journal.36336
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Authors
Keywords
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.
Abstract
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.
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How To Cite (APA)
D Kanishya Gayathri & L Prinslin (September-2023). IMAGE CAPTION GENERATION USING LSTMS AND CONVOLUTIONAL NEURAL NETWORKS. INTERNATIONAL JOURNAL OF NOVEL RESEARCH AND DEVELOPMENT, 8(9), d775-d781. http://doi.one/10.1729/Journal.36336
Issue
Volume 8 Issue 9, September-2023
Pages : d775-d781
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Paper Reg. ID: IJNRD_205981
Published Paper Id: IJNRD2309391
Research Area: Engineering
Author Type: Indian Author
Country: Chennai, Tamilnadu, India
Published Paper PDF: https://ijnrd.org/papers/IJNRD2309391.pdf
Published Paper URL: https://ijnrd.org/viewpaperforall?paper=IJNRD2309391
Crossref DOI: http://doi.one/10.1729/Journal.36336
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