Generate Captions for Traffic Signs Using CNN and LSTM Models

Generate Captions for Traffic Signs Using CNN and LSTM Models

Table of Contents:

1. Introduction
   1. About the Project
   2. Objective of the Project
   3. Data Set Used
2. Understanding CNN and LSTM Models
   1. Convolutional Neural Networks (CNN)
   2. Long Short-Term Memory (LSTM)
   3. Generating Image Features Using CNN
   4. Using LSTM for Sequence Prediction
3. Project Implementation
   1. Code Overview
   2. Importing the Necessary Packages
   3. Loading and Cleaning the Descriptions
   4. Creating Vocabulary from Descriptions
   5. Loading the Dataset
   6. Preprocessing the Data
   7. Encoding the Images and Training the Model
   8. Generating Captions
   9. Evaluating the Model
4. Results and Conclusion
   1. Generated Captions
   2. Project Evaluation
   3. Conclusion

🖼️ Understanding Image Caption Generator with CNN and LSTM Models

In this article, we will explore the concept of an Image Caption Generator using Convolutional Neural Networks (CNN) and Long Short-Term Memory (LSTM) models. We will dive into the implementation of the project, understanding the code, and analyzing the results. So, let's get started!

Introduction

About the Project

Our project is an Image Caption Generator that aims to generate captions for images. We have utilized the concepts of CNN and LSTM models to build a working model. The project team consists of Jay Shrikuluru, Chandu Priyamali, and myself.

Objective of the Project

The main objective of our project is to learn and implement CNN and LSTM models to generate captions for images. By using CNN for image classification and feature extraction, and LSTM for sequence prediction, we can generate Meaningful and accurate captions for a given image.

Data Set Used

For our project, we have used the Flickr 8k data set and the traffic sign data set from Open Images. These data sets provide a wide variety of images with their corresponding captions, which help train and evaluate our model effectively.

Understanding CNN and LSTM Models

Convolutional Neural Networks (CNN)

CNN is a type of deep neural network that is widely used for image classification and recognition tasks. It processes images by scanning them from left to right and top to bottom, extracting important features, and combining them to classify the images. CNN is capable of handling translations, scaling, and changes in perspective.

Long Short-Term Memory (LSTM)

LSTM is a type of recurrent neural network (RNN) that is well-suited for sequence prediction tasks. It can carry Relevant information throughout the processing of inputs and use forget gates to discard non-relevant information. In our project, we utilize LSTM to predict the next WORD based on the previous text.

Generating Image Features Using CNN

To generate image features, we utilize a pre-trained model called Inception v3. CNN scans the images from left to right and top to bottom, pulling out important features. These features are then given as input to the LSTM model.

Using LSTM for Sequence Prediction

LSTM uses the extracted image features and the pre-processed descriptions to predict the sequence of words that form the caption. It leverages the capabilities of RNN to predict the next word based on the context and previous text. By training the model on a large dataset, we can generate captions that are relevant and coherent.

Project Implementation

Code Overview

The code for our Image Caption Generator involves multiple steps. We start by importing the necessary packages and libraries. Then, we load and clean the descriptions. After that, we create a vocabulary from the cleaned descriptions and save them.

The next step involves loading the dataset, which includes the training images and their corresponding captions. We preprocess the data and encode the images using the Inception v3 model. The encoded images are then used along with the preprocessed descriptions to train the LSTM model.

We train the model, extract features from the images, and generate captions. The model is evaluated using various metrics to assess its performance. The weights of the model are saved for future use.

Importing the Necessary Packages

To begin with, we import all the necessary packages and libraries required for our project. These packages include TensorFlow, Keras, NumPy, and others. These packages provide the required functionalities for data processing, model training, and evaluation.

Loading and Cleaning the Descriptions

Before we can use the descriptions for training our model, we need to clean them. Cleaning involves removing punctuation, converting text to lowercase, and removing words that contain numbers. This step ensures that the descriptions are in a suitable format for further processing.

Creating Vocabulary from Descriptions

Once the descriptions are cleaned, we create a vocabulary by separating all the unique words. The vocabulary is created from all the descriptions available. This step helps in mapping each word to a unique index, which is essential for tokenization and training the model.

Loading the Dataset

Loading the dataset involves three important steps: loading the photos, loading the cleaned descriptions, and loading the features extracted from the images. The training dataset contains a large number of images and their corresponding captions. These captions are used for training our model.

Preprocessing the Data

To preprocess the data, we use the Inception v3 model, which is a pre-trained model for image feature extraction. It takes the images as input and generates features that are then used for training the LSTM model. This step enhances the accuracy and relevance of the generated captions.

Encoding the Images and Training the Model

To train our model, we convert the descriptions into padded sequences and split them into input and output sequences. We also encode the images using the pre-trained Inception v3 model. The encoded images and input sequences are used to train the LSTM model.

Generating Captions

The trained model is used to generate captions for new images. We use greedy search to select the most probable word at each step. This process continues until the maximum length of the caption is reached. The generated captions are evaluated for their relevance and accuracy.

Evaluating the Model

To evaluate the performance of our model, we use various metrics such as BLEU score, METEOR score, and CIDEr score. These metrics help us assess how well the model has captured the context and generated captions that are similar to the ground truth captions.

Results and Conclusion

Generated Captions

After training and evaluating our model, we were able to generate captions for images. The captions were meaningful and relevant to the corresponding images. For example, given an image of a speeding vehicle, our model generated the caption "Speed Ahead".

Project Evaluation

Overall, our project was successful in achieving the objective of generating captions for images using CNN and LSTM models. The combination of these models proved to be effective in generating accurate and context-aware captions.

Conclusion

In conclusion, our Image Caption Generator project demonstrated the capabilities of CNN and LSTM models in generating captions for images. The project involved understanding and implementing these models, cleaning and preprocessing the data, training the model, and evaluating the generated captions. Our project opens up possibilities for various applications such as Image Recognition, automatic captioning, and more.

Thank you for reading our article on the Image Caption Generator project!