Breaking Down AI Image Analysis

AI image analysis is the process of using artificial intelligence and other image processing techniques such as computer vision and optical character recognition, to analyze and generate insights from digital images. AI-powered image analysis has revolutionized industries such as healthcare, security, banking, and entertainment by enabling tasks like image classification, facial recognition, and visual content moderation to be carried out quickly and accurately.

In this article, we’ll dive into how AI image analysis works, its applications, as well as limitations and disadvantages.

In this article:

• How Does AI Analyze Images?
• The Applications of AI Image Analysis
• Technologies that Power AI Image Analysis
• Why AI Image Analysis Isn’t Perfect

How Does AI Analyze Images?

An AI image analyzer primarily uses machine learning models, especially deep learning techniques like Convolutional Neural Networks (CNNs). It consists of a sequence of processes or algorithms that break down visual data into mathematical representations, allowing the system to “understand” and perform different tasks on images, including classification, detection, and segmentation.

A typical AI image analysis pipeline consists of the following step-by-step processes:

1. Data collection and image pre-processing: This involves creating a large dataset of images that are relevant to the task, while image processing sets the stage by enhancing and transforming the images and ensuring they are in the right format before feeding them into a model.
2. Feature extraction: Deep learning models (such as CNNs) are applied to the images to detect key features and local patterns such as edges, corners, or textures. The result of this operation is a labeled feature map, which highlights the presence of certain features in specific parts of the image.
3. Model training: The labeled image inputs are passed through several layers in the CNN so that it becomes better at recognizing patterns and generalizing new images.
4. Validation and Testing: After training, the model is evaluated on validation and test datasets to ensure it performs well on unfamiliar data and does not overfit the training data.
5. Deployment: The model is deployed in a real-world setting, where it evaluates the properties of the input image and generates predictions or outputs based on its training data.
6. Fine-tuning: After deployment, the next step is to adjust the model based on test performance and continuously improve the model by retraining on specific subsets of data or with adjusted hyperparameters.

The Applications of AI Image Analysis

AI image analysis is a widely used technology that has revolutionized several industries, including:

• Healthcare – AI image analysis is of significant importance in medical diagnosis. Its applications include analyzing X-rays, MRIs, and CT scans to detect abnormalities in health patients, identifying skin conditions from photographs, and examining tissue samples for cancer detection.
• E-commerce and marketing – AI image analysis can help identify products in images or videos, enhancing search functionality and improving the user experience on e-commerce platforms. For example, users can upload images to obtain products that are identical to objects in the uploaded images. Also, AI image analysis can be used to normalize user-generated data or filter inappropriate content to maintain the quality and safety of e-commerce sites and marketing platforms.
• Banking and finance – AI image analysis is widely used in Know Your Customer (KYC) and other verification processes to verify user documents such as IDs, certificates, etc. It can also be used in fraud detection to identify forged documents or analyze transaction-related image patterns to spot irregularities.
• Security and surveillance – Biometric authentication, pattern recognition, and object detection are extensively used to secure properties, detect threats, and track objects. In agriculture, these technologies enable real-time surveillance to monitor the health of crops and livestock and identify plant and animal diseases early, preventing their spread.
  

  Technologies that Power AI Image Analysis

  Computer vision

  Computer vision is an interdisciplinary field of artificial intelligence that enables computers to understand and analyze visual content, such as images and videos like humans do. Computer vision is concerned with the automatic extraction, analysis, and understanding of useful information from a single image or a sequence of images.

  A typical computer vision program consists of three parts:

  1. Data to train the computer to learn to recognize and distinctively identify objects or patterns
  2. A deep learning algorithm that enables the computer to teach itself about the context of visual data.
  3. A convolutional neural network that helps the computer to interpret and analyze visual data in a way that mimics human vision.

  Computer vision serves as the bedrock for many image analysis tasks, such as image classification, object detection, and tracking. Its applications extend to areas such as manufacturing, healthcare, computer-human interaction, security, and more.

  Convolutional Neural Networks

  A Convolutional Neural Network (CNN) is a type of machine learning model specifically designed to process and analyze visual data. A typical CNN architecture consists of three node layers, namely an input layer, one or more hidden layers, and an output layer. Additionally, a CNN is comprised of three essential components:

  1. Convolution layers: The major mathematical tasks occur within these layers. These layers apply convolution operations to the input data, which involves applying a sliding window function (called a filter or kernel) to a matrix of pixels representing an image and computing dot products to produce a new feature map.
  2. Pooling layers: These are used to reduce the spatial dimensions (width and height) of the feature maps through aggregation operations, which helps decrease the computational load and control overfitting. Common examples of pooling are max pooling and average pooling.
  3. Fully connected layers: Also known as dense layers, these layers connect every neuron in one layer to every neuron in the next layer. After several convolutional and pooling layers, the high-level features are usually flattened into a vector for a final classification or prediction.

  

  A typical CNN Architecture. Source

  CNNs are used in areas such as facial recognition, object recognition, recommender systems, natural language processing, and so on. Some popular CNN architectures include LeNet, AlexNet, VGG, and ResNet.

  Machine and Deep Learning

  Machine learning is a field of study in artificial intelligence that involves using statistical algorithms to identify patterns and make decisions based on data. Image analysis typically requires feature extraction and classification steps using AI models such as CNNs.

  Machine learning approaches are divided into three broad learning categories:

  1. Supervised Learning: In this approach, the model is trained on labeled data, where each input has a corresponding output label. The goal is to learn a mapping from inputs to outputs to make predictions on new, unseen data.
  2. Unsupervised Learning: Here, the model is trained on unlabeled data to identify patterns, clusters, or structures in the data without the need for human intervention.
  3. Reinforcement Learning: This approach involves training a model to make sequential decisions by interacting with an environment by using trial and error.

  Deep learning is often used interchangeably with machine learning; however, there is a marked difference between the two. Deep learning is a subfield of machine learning that leverages artificial neural networks (ANNs), which contain many layers (hence “deep”), to progressively extract higher-level features from raw input data.

  Machine and deep learning are both extensively used in image classification, object detection, and pattern recognition. They have revolutionized image analysis and enabled advancements in fields such as healthcare, manufacturing, security, etc.

  Why AI Image Analysis Isn’t Perfect

  While AI image analysis has made several breakthroughs possible in many fields, it has limitations and disadvantages. Some of these include:

  1. Bias and discrimination: AI models can reflect the biases present in the data they’re trained on, leading to discriminatory results across cultural demographics.
  2. Data dependency: Several AI image analysis processes depends on training the models with a huge set of data in order to obtain accurate results as obtaining and annotating these datasets can be costly, time-consuming, and sometimes impractical.
  3. Computational resources: AI image analysis, especially with deep learning models, requires significant computing power and resources, which can be expensive and inaccessible to some users.
  4. Privacy concerns: Many AI models are trained using publicly available data, some of which contain sensitive information, resulting in privacy intrusions.

  Easy AI Image Analysis with Cloudinary

  With the surge in AI tools over the past two years, a wide range of technologies for image analysis is now available. Cloudinary is a leading cloud-based media management solution with efficient and robust AI for marketers, enterprises, and developers.

  If you’re looking to integrate AI image analysis into your application, you’ll find the Cloudinary AI Content Analysis add-on very useful. The Cloudinary AI Content Analysis add-on provides useful features for image analysis such as automatic image tagging, AI-based image captioning, content-aware detection models, etc.

  Feel free to sign up for a free account today to start exploring what Cloudinary has to offer.