What Is Image Captioning? A Detailed Beginner’s Guide

Gen AI is shaping the digital and radio imaging game.

Be it healthcare, retail, IT or aerospace, image captioning is the building block to analyze, diagnose and solve real-world problems. Inaccurate image captioning signals a gap in data operation workflows and impedes solution mapping to take innovation beyond.

By evaluating and monitoring those gaps with image recognition software, not only businesses analyze and detect image components effectively, but also annotate each vector and pixel that upholds useful and actionable data.

Image captioning is being adopted across areas like satellite imaging, digital visualization, augmented reality marketing and more. Check out how machines can label anything with image captioning and the backend mechanism of it. 

Over time, the machine can be trained to recognize specific elements of an image and apply this knowledge when analyzing other visuals in the future and will use these captions to describe the picture. 

The image captioning process is an important part of image recognition, where the machine is able to identify what exactly the image is about. Using natural language processing, captions are generated that describe in words the different elements that make up the full picture.

The goal is to mimic the human brain as part of a process called computer vision. Artificial neural networks are created to simulate brain neural networks for identifying and assessing visual imagery.

Image captioning types 

There are several different methodologies used in image capturing, depending on the type of AI and the scale needed for the captioning part of an image recognition project. The most common image captioning models are:

• Free-form captioning: This form of captioning allows for creative and free expression in the caption descriptions. The sentences used to describe the image may be unconventional, requiring a greater level of human intervention in the initial stages of training the machine. But, once training is complete, free-form captioning can generate more descriptive and nuanced outcomes.
• Template-based captioning: If you’re still looking for descriptive captions but want greater control over the output, template-based captioning can be useful. It relies on a predefined sequence of caption options, where the machine uses these pre-written descriptions and assigns them to the image accordingly.
• Deterministic models: To ensure consistency with captioning, deterministic models analyze every instance of an image element in every individual image to generate the same caption for that element each time. This consistency is essential in training stages to create accurate and reliable data.
• Stochastic models: Varying captions in the same image may seem unhelpful at first, but can be beneficial for generating more specific and nuanced descriptions. Stochastic model is continually evolving and works on the basis of probabilities when confronted with the same types of elements within a visual. 

How does image captioning work?

As part of generative AI, image captioning is always evolving and becoming more sophisticated. Within the broader field of computer vision, the goal of these tools is to create a bridge between textual and visual information being processed by a machine.

There are five distinct steps that need to be completed during any image captioning project.

1. Gathering and preprocessing data 

Before the machine can start working on new information, pre-processed data must be used to train the algorithm. Current images and their descriptive captions are fed into the machine for training purposes.

As more images are slowly added, the machine gathers a larger vocabulary of descriptive words for future captioning projects. The new images will be preprocessed before entering the system to make the algorithm as accurate as possible. Preprocessing of this data can include resizing, brightening or adjusting contrasts, or scaling the image to make it easier to view.

2. Image encoding 

Using a convolutional neural network (CNN), images are input into the system for the CNN to extract the features before being passed into the next stage for captioning. The encoder is vital in this process as it takes account of the most meaningful features of the image that need to be described.

3. Language decoding

A different type of network, a recurrent neural network (RNN), is typically used at this stage. Variants like long short-term memory (LSTM) or Gated Recurrent Units (GRU) are then deployed to understand the specific vectors extracted during the encoding process. They’ll then take this encoded information and match it to relevant words in the machine’s vocabulary bank. 

While the input might be unintelligible to humans, the output after decoding is a textual caption that describes the different features of the image. As the machine is trained on more data over time, the decoder can begin to predict the next word in a caption sequence based on previous iterations. 

4. Training 

During the training stage, pairs of images and their captions are added to the dataset to allow the machine to understand the content of the images. Generated captions and input captions are separated during training and compared, enabling the machine to learn from its errors and improve accuracy during the next training round.

5. Inference 

Once the training is complete, the image captioning model can generate captions on new images. These images pass through the same stages as during training—first, the image encoder will be used to gather data about the features of the image, and then the language decoder will generate a descriptive caption using the words in its database.

Attention mechanisms are employed throughout each step to help the model narrow its focus on the most relevant parts of the image that need to be described before passing this onto the language decoder for descriptive captioning.

Image captioning uses in business

AI image captioning can be beneficial in numerous ways in a business setting. From healthcare support to marketing and retail, this technology can significantly improve the time it takes for necessary tasks to be completed.

Healthcare

In the medical profession, image captioning can be a powerful tool in diagnosing and treating a range of health conditions. For instance, image captioning of scans like MRIs or CT scans can make processing times for these procedures much faster, which helps both medical professionals and patients make informed decisions quickly.

Retail 

E-commerce stores use AI image captioning to improve the customer shopping experience. Images can be uploaded to online catalogs to help users find similar items based on material, color, pattern, and even fit as determined by image captioning software.

Marketing

Captioning images is an essential task for many digital marketers. It creates an accessible site with descriptive image captions and boosts their search engine optimization (SEO).  

With image captioning tools, marketers can automatically generate captions for both static images and videos which can be used in online marketing materials such as websites and social media. This saves time for marketers to invest in strategic planning that can grow the company’s bottom line.

Agriculture 

Understanding issues with crops as early as possible is one of the most important practices that farmers can use to prevent yield issues or total crop loss. 

Image captioning models can be used to assess the type of disease or growing issue impacting a crop, the symptoms the crop is currently exhibiting, and the degree to which damage has already occurred. When connected to other agricultural systems, farmers can be alerted to these issues timely so they can step in and take action.

Image captioning applications 

Image captioning is being repurposed to mimic human vision and eliminate manual dependency. Let's look at some industry applications of image captioning.

• Accessibility: Image captioning improves image accessibility for visually impaired to derive better understanding and increase concentration. This technology is used in self-assist applications like screen readers, screen talkback, robotic vacuums and so on. The special text-to-speech feature converts content into clear audio.
• Content Moderation: Image captioning is extensively used in web search algorithms to flag inappropriate image or content upload across content distribution platforms. It annotates and categorizes the label and moderate content to comply with browsing guidelines.  
• Autonomous vehicles: The most prominent application of image captioning is the production of self-assist vehicles. Examples like Tesla Autopilot and Robotaxi have a strong ML background that helps detect external objects.
• Medical imaging: Image captioning assists in interpreting medical imaging during pathological tests like X-ray, magnetic resource imaging (MRI) or electro cardio graph (ECG). It derives observed behaviour in human anatomy and improves radiology.
• E-learning: Image captioning is a supervised technique also used to design digital curriculums for educational institutions. This is specially  helpful to students with disabilities or those using assistive devices 
• Computer aided engineering. Image captioning is also included when engineers design digital drafts with CAD software to inspect, tight-fit and mechanize each component for a new device.

Image captioning benefits 

There are numerous benefits that image captioning brings, largely in saving time and helping users avoid human error as much as possible. Additional benefits include:

• Enhancing the user experience: When used in a public-facing setting, image captioning can make content more interesting for users through descriptive captions. This can translate into helping the user understand what they’re viewing, aiding decisions such as finding a similar product to purchase, or allowing a medical team to make a faster decision over patient treatment.
• Assisting with accessibility: Captions on images are essential for users with visual impairments using audio assistance tools. Accurate and detailed descriptions allow them to enjoy a similar user experience to those who can directly see the image on screen.
• Identifying additional features: As humans, we don’t always notice everything in an image. Instead, we usually focus on one or two key features before moving on. With image captioning looking at all elements in the image, we’re able to acknowledge and use additional features that we may not have noticed with our own eyes.

Image captioning challenges

There are also several challenges that come with captioning, as there are with any form of AI and machine learning, including:

• It’s only as good as the training data: The data provided in the initial training stages sets the stage for the algorithm. Errors or inaccuracies can become a significant problem later when the machine is trying to create new captions by itself.
• Inherent biases can skew the algorithm: Similarly, training data often contains human biases, which can create biased outputs. For descriptive image captioning, this could lead to numerous problems like inappropriate descriptions being used in image captions. This can be particularly problematic and require a high level of human intervention to fix if not corrected.
• Real-time processing can be complicated: While many of these AI image tools perform well in real time, the more complex the data set and requirements asked of the captioning program, the more difficult this can become. The many complexities involved in real-time captioning mean that, as of now, this process can still take significant time.

Caption this!

Our world is rapidly becoming more visual, particularly in day-to-day work. As a result, the need to bridge the gap between visual and verbal understanding is becoming more critical. With tools like AI image captioning software, output data can help businesses become more accessible to their customers and give teams time to reallocate focus on other key areas of the business.

Build an algorithm that meets your business needs with data labeling software that annotates and tags your training data quickly and accurately.