Abstract

Image captioning is a multidisciplinary challenge combining computer vision and natural language processing. This study focuses on generating descriptive captions for images using the Flickr8k dataset. The implemented model utilizes a convolutional neural network (CNN) for feature extraction and a long short-term memory network (LSTM) for caption generation. Despite the absence of formal evaluation metrics, the generated captions exhibit a strong alignment with the visual content of the images, showcasing the potential of the applied architecture for real-world applications.

Introduction

Image captioning involves creating textual descriptions for images, requiring a seamless integration of visual understanding and natural language generation. In this work, we utilize the Flickr8k dataset, a collection of 8,000 images annotated with corresponding textual descriptions. By employing a CNN-LSTM-based architecture, we aim to extract meaningful visual features and map them to coherent captions. This study highlights the implementation details, challenges encountered, and insights gained without relying on formal evaluation metrics, focusing instead on qualitative observations.

Methodology

The methodology involves the following steps:

Dataset Preparation: The Flickr8k dataset is preprocessed to extract image features and tokenize captions. Images are resized, and captions are converted into sequences of word indices.

Feature Extraction: A pre-trained CNN (such as InceptionV3) is used to extract high-dimensional features from the images. These features are input into the caption generation model.

Caption Generation: An LSTM network processes the extracted image features and outputs word sequences corresponding to captions. The model is trained with categorical cross-entropy loss.

Training Details: The model is trained using the Flickr8k dataset, with a train-validation split to monitor overfitting. Batch size, learning rate, and other hyperparameters are tuned empirically during the process.

Experiments

The experiments focus on training the model on the Flickr8k dataset. Key configurations include:

Training Data: 6,000 images used for training, with the remaining images reserved for validation and testing.

Model Configuration: The LSTM has a hidden layer size of 256, and a dropout layer is included to prevent overfitting.

Implementation Details: Training is performed using the Adam optimizer with a learning rate of 0.001. No formal evaluation metrics are applied, and qualitative assessments are used to evaluate the model's performance.

Results

The trained model successfully generates descriptive captions for the images in the Flickr8k dataset. While no quantitative metrics were applied, qualitative analysis reveals that the generated captions align well with the visual content. Example captions include phrases like "A dog playing with a ball in the park" or "A child riding a bicycle on a sunny day."