Image Caption Generation Using Deep Learning

Introduction

Image caption generation is an interdisciplinary field that combines computer vision and natural language processing to automatically generate textual descriptions for images. This project proposal aims to develop a deep learning-based system capable of generating accurate and contextually relevant captions for images.

Background

Recent advancements in deep learning have significantly improved the performance of image captioning systems. These systems typically employ an encoder-decoder architecture, where a convolutional neural network (CNN) encodes the image into a feature vector, and a recurrent neural network (RNN) decodes this vector into a descriptive sentence. The use of attention mechanisms further enhances the model's ability to focus on relevant parts of the image when generating captions.

Project Objective

The primary objective of this project is to design and implement a robust image caption generation model that leverages CNNs for feature extraction and RNNs with attention mechanisms for sequence generation. The model aims to achieve high accuracy and fluency in caption generation across diverse datasets.

Methodology

1. Data Collection and Preprocessing

• Datasets: Utilize publicly available datasets such as MS COCO and Conceptual Captions for training and evaluation.
• Preprocessing: Perform data cleaning, normalization, and augmentation to enhance model robustness.

2. Model Architecture

• Encoder: Use a pretrained CNN (e.g., VGG16 or ResNet) to extract feature vectors from images.
• Decoder: Implement an RNN with LSTM units to generate captions, incorporating an attention mechanism to dynamically focus on different parts of the image.

3. Training and Evaluation

• Training: Train the model using cross-entropy loss with backpropagation, employing techniques like dropout and batch normalization to prevent overfitting.
• Evaluation Metrics: Evaluate model performance using BLEU, METEOR, and CIDEr metrics.

Expected Outcomes

The proposed system is expected to generate high-quality captions that accurately describe images across various contexts. By leveraging advanced deep learning techniques, the system should outperform traditional methods in terms of accuracy and fluency.

Conclusion

This project seeks to advance the field of image captioning by developing a state-of-the-art system that effectively integrates CNNs, RNNs, and attention mechanisms. The anticipated improvements in caption quality will contribute to applications in automated content creation, accessibility tools, and visual storytelling.

For further details on related research, please refer to the paper "Image Caption Generation Using Deep Learning," available at Frontiers in Psychology.