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Applications of Image Classification Application #1: Automated inspection and quality control. Application #2: Object recognition in driverless cars. Application #3: Detection of cancer cells in pathology slides. Application #4: Face recognition in security. Application #5: Traffic monitoring and congestion detection.

Let's take an example, you are building a classification model which detects whether an image is of cat or non-cat. So we have different filters used to extract different features from an image like in this case, one filter may learn to detect the eyes of a cat another learn to detect ears and etc.

An image classification model is trained to recognize various classes of images. For example, you may train a model to recognize photos representing three different types of animals: rabbits, hamsters, and dogs. TensorFlow Lite provides optimized pre-trained models that you can deploy in your mobile applications.

Image classification, at its very core, is the task of assigning a label to an image from a predefined set of categories. Practically, this means that our task is to analyze an input image and return a label that categorizes the image. The label is always from a predefined set of possible categories.

Image recognition focuses on identifying and locating specific objects or patterns within an image, whereas image classification assigns an image to a category based on its content. In essence, image recognition is about detecting objects, while image classification is about categorizing images.

Some examples of image classification include: Labeling an x-ray as cancer or not (binary classification). Classifying a handwritten digit (multiclass classification).

Convolutional Neural Networks (CNNs) is the most popular neural network model being used for image classification problem.

Top 5 Classification Algorithms in Machine Learning Logistic Regression. Naive Bayes. K-Nearest Neighbors. Decision Tree. Support Vector Machines.

In the image classification field, traditional machine learning algorithms, such as K-Nearest Neighbor (KNN) and Support Vector Machine (SVM), are widely adopted to solve classification problems and especially perform well on small datasets.

Digital image classification uses the spectral information represented by the digital numbers in one or more spectral bands, and attempts to classify each individual pixel based on this spectral information. This type of classification is termed spectral pattern recognition.