Algorithms and models of Image Classification

There isn’t one straightforward approach for achieving image classification, thus we will take a look at the two most notable kinds: supervised and unsupervised classification.

Supervised Classification

Supervised learning is well-known for its intuitive concept – it operates like an apprentice learning from a master. The algorithm is trained on a labeled image dataset, where the correct outputs are already known and each image is assigned to its corresponding class. The algorithm is the apprentice, learning from the master (the labeled dataset) to make predictions on new, unlabeled data. After the training phase, the algorithm uses the knowledge gained from the labeled data to identify patterns and predict the classes of new images.

• Supervised algorithms can be divided into single-label classification and multi-label classification. Single-label classification assigns a single label to an image, which is the most common type. Multi-label classification, on the other hand, allows an image to be assigned multiple labels, which is useful in fields like medical imaging where an image may show several diseases or anomalies.
• Famous supervised classification algorithms include k-nearest neighbors, decision trees, support vector machines, random forests, linear and logistic regressions, and neural networks.
• For instance, logistic regression predicts whether an image belongs to a certain category by modeling the relationship between input features and class probabilities. K-nearest neighbors (KNN) assigns labels based on the closest k data points to the new input, making decisions based on the majority class among the neighbors. Support vector machines (SVM) find the best separating boundary (hyperplane) between classes by maximizing the margin between the closest points of each class. Decision trees use a series of questions about the features of the data to make classification decisions, creating a flowchart-like model.

Unsupervised Classification

Unsupervised learning can be seen as an independent mechanism in machine learning; it doesn’t rely on labeled data but rather discovers patterns and insights on its own. The algorithm is free to explore and learn without any preconceived notions, interpreting raw data, recognizing image patterns, and drawing conclusions without human interference.

• Unsupervised classification often employs clusterization, a technique that naturally groups data into clusters based on their similarities. This method doesn’t automatically provide a class; rather, it forms clusters that need to be interpreted and labeled. Notable clusterization algorithms include K-means, Mean-Shift, DBSCAN, Expectation–Maximization (EM), Gaussian mixture models, Agglomerative Clustering, and BIRCH. For instance, K-means starts by selecting k initial centroids, then assigns each data point to the nearest centroid, recalculates the centroids based on the assigned points, and repeats the process until the centroids stabilize. Gaussian mixture models (GMMs) take a more sophisticated approach by assuming that the data points are drawn from a mixture of Gaussian distributions, allowing them to capture more complex and overlapping data patterns.
• Among the wide range of image classification techniques, convolutional neural networks (CNNs) are a game-changer for computer vision problems. CNNs automatically learn hierarchical features from images and are widely used in both supervised and unsupervised image classification tasks.

In today’s digital era, where visual data is abundantly generated and consumed, image classification emerges as a cornerstone of computer vision. It enables machines to interpret and categorize visual information, a task that is pivotal for numerous applications, from enhancing medical diagnostics to powering autonomous vehicles. Understanding image classification, its working mechanisms, and its applications can provide a glimpse into the vast potential of artificial intelligence (AI) in transforming our world.