Image Classification Techniques

Image classification refers to a process in computer vision that can classify an image according to its visual content.

Introduction

Today, with the increasing volatility, necessity, and applications of artificial intelligence, fields like machine learning, and its subsets, deep learning, and neural networks have gained immense momentum. The training needs software and tools like classifiers, which feed huge amount of data, analyze them, and extract useful features. The intent of the classification process is to categorize all pixels in a digital image into one of several classes. Normally, multi-spectral data are used to perform the classification and, indeed, the spectral pattern present within the data for each pixel is used as the numerical basis for categorization. The objective of image classification is to identify and portray, as a unique gray level (or color), the features occurring in an image in terms of the object these features actually represent on the ground. Image classification is perhaps the most important part of digital image analysis. Classification between objects is a complex task and therefore image classification has been an important task within the field of computer vision. Image classification refers to the labelling of images into one of a number of predefined classes. There are potentially n number of classes in which a given image can be classified. Manually checking and classifying images could be a tedious task especially when they are massive in number and therefore it will be very useful if we could automate this entire process using computer vision. The advancements in the field of autonomous driving also serve as a great example of the use of image classification in the real-world. The applications include automated image organization, stock photography and video websites, visual search for improved product discoverability, large visual databases, image and face recognition on social networks, and many more; which is why, we need classifiers to achieve maximum possible accuracy.

Structure for performing Image Classification

1. Image Pre-processing: The aim of this process is to improve the image data (features) by suppressing unwanted distortions and enhancement of some important image features so that the computer vision models can benefit from this improved data to work on. Steps for image pre-processing includes Reading image, Resizing image, and Data Augmentation (Gray scaling of image, Reflection, Gaussian Blurring, Histogram, Equalization, Rotation, and Translation).
2. Detection of an object: Detection refers to the localization of an object which means the segmentation of the image and identifying the position of the object of interest.
3. Feature extraction and training: This is a crucial step wherein statistical or deep learning methods are used to identify the most interesting patterns of the image, features that might be unique to a particular class and that will, later on, help the model to differentiate between different classes. This process where the model learns the features from the dataset is called model training.
4. Classification of the object: This step categorizes detected objects into predefined classes by using a suitable classification technique that compares the image patterns with the target patterns.

Supervised Classification

Supervised classification is based on the idea that a user can select sample pixels in an image that are representative of specific classes and then direct the image processing software to use these training sites as references for the classification of all other pixels in the image. Training sites (also known as testing sets or input classes) are selected based on the knowledge of the user. The user also sets the bounds for how similar other pixels must be to group them together. These bounds are often set based on the spectral characteristics of the training area. The user also designates the number of classes that the image is classified into. Once a statistical characterization has been achieved for each information class, the image is then classified by examining the reflectance for each pixel and making a decision about which of the signatures it resembles most. Supervised classification uses classification algorithms and regression techniques to develop predictive models. The algorithms include linear regression, logistic regression, neural networks, decision tree, support vector machine, random forest, naive Bayes, and k-nearest neighbor.

Unsupervised Classification

Unsupervised classification is where the outcomes (groupings of pixels with common characteristics) are based on the software analysis of an image without the user providing sample classes. The computer uses techniques to determine which pixels are related and groups them into classes. The user can specify which algorithm the software will use and the desired number of output classes but otherwise does not aid in the classification process. However, the user must have knowledge of the area being classified when the groupings of pixels with common characteristics produced by the computer have to be related to actual features on the ground. Some of the most common algorithms used in unsupervised learning include cluster analysis, anomaly detection, neural networks, and approaches for learning latent variable models.

Convolutional Neural Network

Convolutional Neural Network (CNN, or ConvNet) are a special kind of multi-layer neural networks, designed to recognize visual patterns directly from pixel images with minimal pre-processing. It is a special architecture of artificial neural networks. Convolutional neural network uses some of its features of visual cortex and have therefore achieved state of the art results in computer vision tasks. Convolutional neural networks are comprised of two very simple elements, namely convolutional layers and pooling layers. Although simple, there are near-infinite ways to arrange these layers for a given computer vision problem. The elements of a convolutional neural network, such as convolutional and pooling layers, are relatively straightforward to understand. The challenging part of using convolutional neural networks in practice is how to design model architectures that best use these simple elements. The reason why convolutional neural network is hugely popular is because of their architecture, the best thing is there is no need of feature extraction. The system learns to do feature extraction and the core concept is, it uses convolution of image and filters to generate invariant features which are passed on to the next layer. The features in next layer are convoluted with different filters to generate more invariant and abstract features and the process continues till it gets final feature/output which is invariant to occlusions. The most commonly used architectures of convolutional neural network are LeNet, AlexNet, ZFNet, GoogLeNet, VGGNet, and ResNet.

Artificial Neural Network

Inspired by the properties of biological neural networks, Artificial Neural Networks are statistical learning algorithms and are used for a variety of tasks, from relatively simple classification tasks to computer vision and speech recognition. Artificial neural networks are implemented as a system of interconnected processing elements, called nodes, which are functionally analogous to biological neurons. The connections between different nodes have numerical values, called weights, and by altering these values in a systematic way, the network is eventually able to approximate the desired function. The hidden layers can be thought of as individual feature detectors, recognizing more and more complex patterns in the data as it is propagated throughout the network. For example, if the network is given a task to recognize a face, the first hidden layer might act as a line detector, the second hidden takes these lines as input and puts them together to form a nose, the third hidden layer takes the nose and matches it with an eye and so on, until finally the whole face is constructed. This hierarchy enables the network to eventually recognize very complex objects. The different types of artificial neural network are convolutional neural network, feedforward neural network, probabilistic neural network, time delay neural network, deep stacking network, radial basis function network, and recurrent neural network.

Support Vector Machine

Support vector machines (SVM) are powerful yet flexible supervised machine learning algorithms which are used both for classification and regression. Support vector machines have their unique way of implementation as compared to other machine learning algorithms. They are extremely popular because of their ability to handle multiple continuous and categorical variables. Support Vector Machine model is basically a representation of different classes in a hyperplane in multidimensional space. The hyperplane will be generated in an iterative manner by support vector machine so that the error can be minimized. The goal is to divide the datasets into classes to find a maximum marginal hyperplane. It builds a hyper-plane or a set of hyper-planes in a high dimensional space and good separation between the two classes is achieved by the hyperplane that has the largest distance to the nearest training data point of any class. The real power of this algorithm depends on the kernel function being used. The most commonly used kernels are linear kernel, gaussian kernel, and polynomial kernel.

K-Nearest Neighbor

K-Nearest Neighbor is a non-parametric method used for classification and regression. In both cases, the input consists of the k closest training examples in the feature space. It is by far the simplest algorithm. It is a non-parametric, lazy learning algorithm, where the function is only approximated locally and all computation is deferred until function evaluation. This algorithm simply relies on the distance between feature vectors and classifies unknown data points by finding the most common class among the k-closest examples. In order to apply the k-nearest Neighbor classification, we need to define a distance metric or similarity function, where the common choices include the Euclidean distance and Manhattan distance. The output is a class membership. An object is classified by a plurality vote of its neighbors, with the object being assigned to the class most common among its k nearest neighbors (k is a positive integer, typically small). If k = 1, then the object is simply assigned to the class of that single nearest neighbor. Condensed nearest neighbor (CNN, the Hart algorithm) is an algorithm designed to reduce the data set for K-Nearest Neighbor classification.

Naïve Bayes Algorithm

Naive Bayes classifiers are a collection of classification algorithms based on Bayes’ Theorem. It is not a single algorithm but a family of algorithms where all of them share a common principle, i.e. every pair of features being classified is independent of each other. Naive Bayes is a simple technique for constructing classifiers: models that assign class labels to problem instances, represented as vectors of feature values, where the class labels are drawn from some finite set. All naive bayes classifiers assume that the value of a particular feature is independent of the value of any other feature, given the class variable. Naive Bayes algorithm is a fast, highly scalable algorithm, which can be used for binary and multi-class classification. It depends on doing a bunch of counts. It is a popular choice for text classification, spam email classification, etc. It can be easily trained on small dataset. It has limitation as it considers all the features to be unrelated, so it cannot learn the relationship between features. Naive Bayes can learn individual features importance but can’t determine the relationship among features. Different types of naïve bayes algorithms are gaussian naïve bayes, multinomial naïve bayes, and bernoulli naïve bayes.

Random Forest Algorithm

Random forest is a supervised learning algorithm which is used for both classification as well as regression. As we know that a forest is made up of trees and more trees means more robust forest, similarly, random forest algorithm creates decision trees on data samples and then gets the prediction from each of them and finally selects the best solution by means of voting. It is an ensemble method which is better than a single decision tree because it reduces the over-fitting by averaging the result. The random forest is a classification algorithm consisting of many decision trees. It uses bagging and feature randomness when building each individual tree to try to create an uncorrelated forest of trees whose prediction by committee is more accurate than that of any individual tree.

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