Image Classification

What is Image Classification?

Image classification is a process that uses machine learning (ML) to analyze an image and determine its main subject. Image classification plays an important role in more advanced computer vision tasks such as object detection and object localization.

Key Takeaways

Image classification is an important building block for more complex computer vision tasks that interpret visual information.
Deep learning algorithms are used to predict classifications (class labels).
The algorithms use feature extraction to predict the most appropriate class label.
To become accurate, complex algorithms need to be trained with millions of images.
In order to facilitate accurate data labeling, publicly available datasets are often used for model training.

How Image Classification Works

Image classification uses deep learning algorithms to extract relevant features from an image and predict, based on those features, what high-level category (class) the image belongs to.

Once trained, the model can then be presented with new, unlabeled images and predict their categories based on the patterns it has learned.

On the back end, the category may be accompanied by a confidence score that indicates how sure the model is of its classification. The accuracy of the predictions depends on the quality and quantity of the training data, as well as the complexity of the model itself.

How Image Classification Works

Types of Image Classification

Image classifications can be binary, multiclass, or multilabel.

Binary Classification

There are two classes to choose from, and the model predicts which one an image belongs to.
Example: Digital photo or AI-generated image.

Multiclass Classification

There are several related classes to pick from, and the model predicts the best class.

Example: Labrador, Poodle, Chihuahua, Great Dane.

Image Classification Algorithms and Models

Image classification algorithms can be either hierarchical or flat.

Flat algorithms directly predict the final class of an image. Hierarchical algorithms first predict the most general class and then progressively narrow down the prediction to more specific subclasses at lower levels.

The choice between flat and hierarchical algorithms depends on the specific problem and the trade-off between speed and accuracy. Flat algorithms are generally preferred for simpler problems with a limited number of classes, while hierarchical algorithms can offer better performance and interpretability for complex problems with a large number of classes and a clear hierarchical structure.

How Image Classification Models are Trained

Image classification models can be trained with supervised, unsupervised, semi-supervised, or self-supervised (transfer) techniques.

Supervised Classification Models

Supervised classification requires new models to be trained with labeled images. The amount of labeled data needed depends on the complexity of the task and the diversity of the images the model is expected to classify.

Simple tasks with few classes might require hundreds of images per class, while complex tasks with a large number of classes might require millions of labeled images.

Unsupervised Classification Models

Unsupervised classification uses unlabeled images to train a model. The model analyzes the images to find patterns and uses the patterns to cluster similar images together in a class. This technique uses a human in the loop (HiTL) strategy to assign a label to each class.

Semi-Supervised Classification Models

Semi-supervised classification models use both labeled and unlabeled data. Essentially, the model uses the labeled data to guide the learning process for a larger unlabeled data set. This technique is useful when labeled images are scarce or expensive.

Self-Supervised Classification Models

Self-supervised models are trained on a foundation model and use transfer learning to predict classes for new, unseen images.

For example, zero-shot classification models are trained with textual descriptions of different classes they need to recognize. This technique allows the model to recognize classes they haven’t explicitly seen during training.

CNN Image Classification

Most image classification models today use convolutional neural network (CNN) algorithms. CNNs have a unique architecture that is specifically designed for processing and classifying images. This type of learning algorithm can also be adapted for related tasks like object detection.

Image Classification vs. Object Detection

Image classification and object detection are both important tasks in computer vision, but they each have different objectives.

Image classification predicts the image’s main subject. Object detection predicts what objects are in the image.

Techniques Used in Image Classification Assessment

Machine learning engineers can use a variety of assessment techniques to understand the strengths and weaknesses of their image classification models. This information is essential for improving the model’s performance, addressing biases, and ensuring the model’s effectiveness in real-world applications.

Important assessment techniques include metrics that state:

Accuracy

The proportion of correctly classified instances over the total instances.

Precision

The ratio of true positives to the sum of true positives and false positives.

Recall

The ratio of true positives to the sum of true positives and false negatives.

F1-Score

The harmonic mean of precision and recall.

Image Classification Applications

Image classification has many applications in other computer vision tasks, including:

Object detection
Object counting
Image segmentation
Image retrieval
Facial recognition
Medical image analysis
Action recognition

Image Classification Examples

Image classification has a wide range of uses across various industries and fields of study. For example, the technology can be used to categorize satellite or aerial images into distinct land cover classes such as forests.

Image classification in remote sensing is a powerful tool for monitoring deforestation and tracking changes in land use over time.

Image classification is also used to categorize images users publish on social media sites. For example, Facebook and Instagram use image classification to identify images that don’t meet their acceptable use standards. Object detection can further enhance this process by predicting specific elements that contribute to an image’s classification.

Image Classification Pros and Cons

Image classification models, like any type of artificial intelligence (AI), have both advantages and disadvantages.

Pros

Automates the process of categorizing images
Can handle large volumes of image data efficiently
Supports a wide range of computer vision tasks

Cons

Insufficient or biased training data can lead to inaccurate predictions
Training complex image classification models from scratch requires significant computational resources and can be expensive
Image classification models are vulnerable to adversarial attacks

The Bottom Line

The evolving nature of artificial intelligence (AI) makes it necessary to continually reevaluate image classification’s meaning to include new techniques and technologies. For example, in the future, image classification models are likely to be integrated with object detection models, and object detection models will be integrated with object counting and object localization models.