International Research Journal of Engineering and Technology (IRJET)
e-ISSN: 2395-0056
Volume: 11 Issue: 10 | Oct 2024
p-ISSN: 2395-0072
www.irjet.net
Image Description Generation using Deep Learning Prof. Sukruti M.Dad1,Arya Narale2, Swapnil Sawant3,Aakanksha Shahane4, Pratik Sonawane 5 1Dept. of Information Technology(I2IT) Savitribai Phule Pune University Pune
2Dept. of Information Technology(I2IT) Savitribai Phule Pune University Pune
3Dept. of Information Technology(I2IT) Savitribai Phule Pune University Pune
4Dept. of Information Technology(I2IT) Savitribai Phule Pune University Pune
5Dept. of Information Technology(I2IT) Savitribai Phule Pune University Pune
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Abstract - Image description generation, often referred
Earlier approaches to image captioning relied on handcrafted feature extraction and statistical models, which were limited in their ability to capture the complexity and variability found in real-world images and natural language. With the success of CNNs in visual recognition tasks, deep learning brought a transformative shift, allowing models to automatically learn hierarchical representations of visual content. CNNs are particularly effective at identifying spatial hierarchies and complex patterns within images, making them ideal for extracting detailed visual features. However, while CNNs are effective at interpreting the “what” in an image, they lack the linguistic capacity to articulate these observations as coherent sentences.
to as image captioning, is a key area of research within artificial intelligence focused on creating textual descriptions for visual content. This capability has broad applications, including assisting visually impaired individuals, enhancing search engine functionalities, and improving social media user experiences. Previously, image description generation was largely reliant on manual feature extraction and rule-based techniques, which limited its scalability and adaptability. However, with advancements in deep learning, models like Convolutional Neural Networks (CNNs) and Bidirectional Encoder Representations from Transformers (BERT) have become essential tools, leveraging large-scale data to learn both visual and language features autonomously. CNNs are wellsuited to capturing spatial patterns in images, enabling an understanding of fine visual details necessary for interpreting context within an image. BERT, a transformerbased model trained on extensive text datasets, enhances language generation by producing coherent and contextually accurate sentences. This project explores an integrated approach using CNNs for visual feature extraction and BERT for transforming these features into descriptive textual output. By combining the strengths of CNNs and BERT, we aim to produce more accurate, detailed, and contextually relevant image captions. Extensive experiments will evaluate the CNN-BERT model’s performance compared to traditional methods, focusing on improvements in descriptive precision, coherence, and computational efficiency.
BERT, a transformer-based language model developed by Google, has demonstrated exceptional capabilities in language comprehension and generation tasks. Its bidirectional architecture allows it to capture deep contextual relationships within text, making it a powerful tool for generating grammatically accurate and contextually meaningful sentences. This project takes advantage of the strengths of CNNs for image feature extraction and BERT for language generation, creating a robust system for image captioning that combines visual understanding with linguistic fluency. The main goal of this project is to build and evaluate a deep learning framework for image description generation, utilizing CNN for extracting image features and BERT for generating natural language descriptions. The proposed approach consists of a two-stage process: the CNN model first processes the image to extract visual features, which are then passed to a BERT-based language generation model. This combination allows for highquality, fluent, and descriptive captions that adapt to a wide range of image content. Using popular datasets such as MS COCO and Flickr30k, we aim to assess the effectiveness of our model compared to established benchmarks, measuring improvements in descriptive accuracy, fluency, and computational efficiency. This study’s findings are expected to contribute to multimodal AI research, providing insights into the integration of visual and textual models for practical applications in image captioning and beyond.
Key Words: Image Description, CNN, BERT, Deep Learning, Multimodal Learning, Image Captioning.
1.INTRODUCTION The rapid growth of deep learning has reshaped fields like computer vision and natural language processing, enabling models to perform tasks once considered exclusively human, such as generating descriptive captions for images. Image description generation, or image captioning, has emerged as a highly impactful task with potential applications in assistive technologies for visually impaired individuals, automatic annotation of multimedia content, and enhanced image search and recommendation systems on social media platforms.
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