# Image Classification Techniques

Explore the cutting-edge techniques used in image classification.

There are various techniques and strategies for image classification. Each of these techniques has advantages and disadvantages. Some methods will outperform others depending on the type of problem This lesson will introduce four image classification techniques:

- Fuzzy logic
- Genetic algorithm
- Support vector machine (SVM)
- Neural network

Let’s learn about these techniques in detail.

## Fuzzy logic

**Fuzzy logic** is a heuristic approach that allows better decision tree processing. It also integrates easily with rules-based programming.

Fuzzy logic is different from standard logic because it has a truth value of one or zero for all statements. Fuzzy logic generalizes statements differently because it allows partial truth for all statements. Instead of one or zero, it accepts floating points such as 0.5 or 0.7.

### Advantages

Since fuzzy logic is stochastic, we can easily identify different stochastic relationships to describe the properties and features.

### Disadvantages

This technique is highly dependent on prior knowledge of decision boundaries. As a result, if the direction of the decision is’nt clear from the start, we won’t get reliable results. The following illustration shows the difference between Boolean logic and fuzzy logic:

## Genetic algorithm

The **genetic algorithm** relies on the concepts of natural selection and genetics. It’s able to generate acceptable solutions for optimization and search problems.

### Advantages

Genetic algorithms can handle complex, non-differentiable, and multi-model spaces. They’re a highly efficient method for solving complex problems. They’re usable in both feature classification and feature selection. They can refine any irrelevant or noisy features selected for classification. Unlike other algorithms, this method won’t always be optimal globally.

We can imagine how a greedy algorithm could yield locally optimal solutions that may not be the best globally.

### Disadvantages

As mentioned earlier, genetic algorithms don’t always find the best solution, which makes them undesirable for most image classification problems. It’s not the most efficient technique to find local optima.

The image below illustrates the concept behind the genetic algorithm:

## Support vector machine (SVM)

The **support vector machine (SVM)** is designed to create either a decision boundary or the best line that can segregate n-dimensional space into classes. The line or boundary is also known as a **hyperplane**.
The dimension of the hyperplane depends on the number of features. For example, a hyperplane will be a line if there are two dimensions, and a 2-D plane if there are three dimensions.

### Advantages

SVM is an excellent choice because it reduces the computational complexity of any quadratic optimization problem. Also, we can control the complexity of the decision rules and the frequency of errors.

Furthermore, this algorithm is less likely to be overfitted and works on a limited dataset.

### Disadvantages

Since the training data is not linearly separable, it’s quite difficult to determine optimal parameters for SVM. The training time is also slower than other algorithms.

The following illustration serves as a reference for the support vector machine:

## Neural network

A **neural network** is a weighted graph in which nodes represent neurons and interconnected edges represent the connections. Each edge contains a value indicating its weight.

This algorithm recognizes hidden patterns and identifies the correlations in the input data.

### Advantages

A neural network is quite flexible. We can train a model for either classification or regression. In addition, it can be represented as a boolean function. This includes AND, OR, and NOT.

Unlike other algorithms, the neural network is resilient to noisy inputs.

### Disadvantages

It’s difficult to understand a neural network and determine its optimal structure. It requires a lot of trial and error. A neural network is prone to overfitting if there are too many attributes.

Neural networks often require a large number of datasets to be trained. As a result, the performance of a neural network model is usually slightly better than other algorithms when trained with the same datasets.

The following illustration represents the neural network as a weighted graph:

There are multiple techniques for image classification. This course focuses on the neural network technique.