In simple terms, classification is the organization or arrangement of objects, documents, etc., in categories based on their features. When the same concept is applied to images, it is referred to as Image Classification. However, basic Image Classification is an application of Machine Learning, a field of Artificial Intelligence, which was invented to bridge the gap between computer vision and human vision. By categorizing images based on their features, and storing this information in a database, we allow the system to automatically detect an object from its image the next time it encounters it.
Image classification using deep learning is revolutionizing business operations across various sectors. From the education sector to improving medical imagery, strengthening security systems, and even expediting maintenance tasks in the aviation sector, all crucial operations are utilizing this technology today. Thus, in this blog, let’s learn how image classification based on deep learning works, and how it can contribute to making your life simpler. Let’s get started.
As far as image classification using machine learning is concerned, it involves two important modules. The first is a feature extraction module where different important components of an image are separated such as textures, edges, angles, etc. The second module is the classification module where depending on these important features an image is placed into a relevant category. A big problem in this process was that extraction of only a specific set of features was possible, and many times the important differentiating features got left out.
To overcome this challenge, image classification with deep learning was introduced. Deep learning is a subset of machine learning that involves learning through its own method of computing. Deep learning breaks down a piece of information persistently in a homogenous manner. Without going too deep into technical details, by virtue of the design of a deep neural network, it is able to learn many features/ characteristics of an image unlike simple machine learning. This allows a deep learning model to make better determinations as it is capable of extracting and assessing almost all characteristics of an image. Hence, deep learning is considered more capable than basic machine learning for image classification.
The basic structural and functional unit of the human nervous system is called a neuron. Multiple neurons synapse (connect) together to form a network through which impulses get transferred throughout the body. Based on a similar architecture, a network comprising algorithms that recognizes patterns and interprets sensory data with a ‘machine perception’ is called an Artificial Neural Network.
A multi-layered neural network is responsible for image classification using deep learning. In a nutshell, an input image is fed to the neural network, and the network is trained by conveying the details of the required output. This forms the basis of image classification through deep learning models. By using the right code, you can use artificial neural networks for image classification using deep learning GitHub.
The Convolutional Neural Network or CNN is the most popular algorithm which is currently used to implement deep learning models for computer vision across applications. Convoluted Neural Networks are considered the backbone of image classification. A Convoluted Neural Network contains three layers- input, output, and hidden layers, respectively. All these three levels are interconnected and are responsible for the processing needed to classify images.
By implementing CNNs, you can perform image classification using deep learning in python.
Let us assume that a coloured image of a giraffe is fed as an input to a Convolutional Neural Network. Here, if the image has a size of 200×200, the computer will focus on processing three numerical values- 200 height, 200 width, and 3 RGB channel values (200×200×3). When this image is entered into a CNN, every pixel of the image is given a value between 0 and 255, denoting the intensity of the colour of each pixel.
The next step is where the computer searches for base-level features to identify the subject in the image. These features can be edges, curvatures, angles, etc. As the processing continues, the image of the giraffe is passed through more convolutional layers, more characteristics are identified, and finally, an output is generated. A fully connected layer is attached at the end, which extracts the output information regarding the number of feature classes, from which the deep learning model can select any desired class and identify the giraffe in the image.
A key step in Intelligent Data Processing (IDP) is the document classification phase. Here, the aim is to divide the documents into different categories based on their format, size, and even content. It allows better recognition, and makes the documents easily searchable as well.
This phase of IDP can be streamlined and expedited by performing image classification using deep learning pdf. If document scans or images are available, deep learning algorithms can easily help in extracting features and recognizing documents based on image patterns.
Document image classification using deep learning makes the follow-up extraction simpler. This way, data from every document gets integrated with the desirable workflow in much less time. Overall, with deep learning-based image classification, business operations become smoother.
KlearStack provides future-ready solutions for Intelligent Data Processing that not just focus on introducing automation in business setups, but also on providing the benefits of Artificial Intelligence to process even semi-structured/ unstructured documents.
KlearStack’s Intelligent Data Processing makes use of OCR, AI, and Deep Learning to make routine tasks of data handling and processing much more simple. Image Classification using deep learning is an integral part of our service, where scanned documents are read, matched with our system database, and then recognized to process the information faster.