Integrating Computer Vision with Natural Language Processing for Image Understanding

5 min readAug 14, 2023

Introduction

Computer vision and natural language processing (NLP) are prospering domains of artificial intelligence that has obtained a lot of popularity in recent days. Computer vision enables computers to interpret and comprehend images and videos just like biological visual system, whereas natural language processing deals with understanding and generating languages as like humans can and do.

Integrating Computer Vision(CV) with Natural Language Processing(NLP) enhances machines to not only witness the image but to understand the text embedded in it. This interdisciplinary fusion has led to significant advancements in image understanding, semantic analysis, generating image captions etc.

Image understanding refers to the ability of a computer to grasp and make sense of the visual information present in images . The goal of image understanding is to enable machines to extract relevant information, recognize objects, scenes, and patterns, and ultimately comprehend the visual world in ways similar to human perception.

Image understanding

Image understanding is the process of analysing what’s displayed on the image and also to comprehend what’s happening in the scene. Image understanding extends beyond basic image processing; wherein image processing we use low level operations like edge detection, filtering ,whereas in image understanding the goal is to extract higher level information and insights from images allowing machines to capture the patterns and relationships and depict the visual content.

Given a goal, or a reason for looking at a particular scene, these systems produce descriptions of both the images and the world scenes that the images represent.”
– Image Understanding, by J.K. Tsotos. In Encyclopedia of Artificial Intelligence

Existing approaches can be classified as:

1. Top — down approach — which starts from just of an image and convert it to words

2. Bottom — up approach — which comes up with words describing various aspects of image and then combining them together to make an image

1.Image Captioning:

Image captioning is the process of automating the generation of textual description about a particular image or a scene. This kind of process uses Encoder-Decoder architecture where the encoder extracts the features from the image and the decoder make the phrases accordingly.

Encoders are usually made of Convolutional Neural Network (CNN) to extract local level information from the image, The last hidden state of the CNN is connected to the decoder, The decoder usually might be Recurrent Neural Network (RNN) which bundle up the words with respect to the training corpus.

Image Caption Model with Attention

The model is made up of four logical components:

Encoder
Sequence Decoder
Attention Mechanism
Sentence Generator

We encode the parts responsible for processing the input image and extracting relevant Features.

The sequence decoder is responsible for generating the caption one word at a time using RNN The decoder RNN digest the previous generated words along with the encoded image speeches to predict the next word in the sequence.

Attention mechanism enhances the performance, allowing the decoder to focus on various parts of the image while generating each word in the caption. This is particularly helpful when image contains multiple Regions of interest. Attention mechanism help the model align words with the relevant image features.

Sentence generator is the combination of the Sequence Decoder and the attention mechanism, It takes the encoded image features ,the previously generated words, and the attention weights as inputs and generates the captions sequentially.

2. Text-to-Image Generation

The goal of Text to image generation is to generate an image based on the given prompt or textual description.

Generating an image from a given text description has two goals: visual realism and semantic consistency. Although significant progress has been made in generating high-quality and visually realistic images using generative adversarial networks
— MirrorGAN: Learning Text-to-image Generation by Redescription
— Tingting Qiao Jing Zhang , Duanqing Xu1, and Dacheng Tao
— CVPR 2019

Image generation can be synthesised by using Generation Adversarial Network (GAN) Which uses generator-discriminator Architecture to train and generate Images

As mentioned earlier it consists of two Neural networks:

1. Generator

2. Discriminator

that works together in order to produce realistic images from textual descriptions or prompts

The role of generator is to take input as a text and to generate an image using the combination of CNN and RNN. The Generators goal is to produce images in such a way that they are indistinguishable from real images which then further evaluated by the discriminator

Discriminator on the other hand used to distinguish real images from dataset and the images generated by the generator. The discriminator provides feedback to the generator by indicating the Originality of the image

To wrap things up both image captioning and text to image generation are two related tasks that leverage both computer vision and natural language processing techniques and act as a bridge between visual content and textual description

Real-Life Use Cases

1. In healthcare image captioning can be used to offer descriptions of medical images making them more comprehensible to patients by interpreting the clinical images.

2. Image captioning can be helpful in providing detailed descriptions about the scene for visually impaired people

3. Image captioning can be withheld in search engines by automatically generating textual descriptions for the image search

4. Text to image generation (T2I) can be helpful to create appealing visuals by means of giving appropriate prompt

5. (T2I) can be helpful in creating particular images for web content like articles blog posts and presentation with enriched details generated by AI

6. (T2I) can be helpful in architectural visualisation to visualise the buildings and the structures in actual construction

7. (T2I) can be used to create detailed graphs charts to interpret real time data

Here are some of the AI websites that offer the above mentioned functionalities :

Image Captioning AI

1. Taption

2. aiimag

3. Eye for AI

4. AVA

Image Generator AI

1. DALL-E-2

2. Deep AI

3. Mid Journey

4. Dream Studio

Conclusion and Future Prospects

Integration and inter-operability of computer vision and NLP has a significant contribution, not only by utilising the state-of-the-art models but also creating new challenges to cope up with in day-to-day life. Its presence is prominent in each and every field, right from medicine, autonomous vehicles, e-commerce, entertainment image understanding has made a notable impact. Future Prospects can delve into advancements in Deep Learning, Domain Specific Progress, Meta — Learning etc.