AI Image Recognition with Convolutional Neural Networks (CNNs)

AI Image Recognition with Convolutional Neural Networks (CNNs)

Deep learning techniques are based on neural networks, which are a subset of machine learning. They consist of node layers with one or more hidden levels, an input layer, and an output layer. Every node has a threshold and weight that are connected. A node is activated and sends data to the following layer of the network if its output exceeds a given threshold value. If not, no data is transferred to the network's subsequent tier. 

What is a Convolutional Neural Network (CNN)? 

Convolutional neural networks (CNNs) are regularized kinds of feed-forward neural networks that use filter (or kernel) optimization to teach themselves feature engineering. Using regularized weights over fewer connections prevents the exploding and disappearing gradients that were observed during backpropagation in previous neural networks. For instance, processing a 100 × 100-pixel image would require 10,000 weights for each neuron in the fully connected layer. Nevertheless, only 25 neurons are needed to compute 5x5-sized tiles when using cascaded convolution (or cross-correlation) kernels. More context windows are used to extract higher-layer features than lower-layer characteristics. 

This neural network model recognizes and processes patterns in images using ideas from linear algebra, particularly matrix multiplication. This model's convolutional layers can produce feature maps that pinpoint a particular region inside a visual input. After that, the website is dissected and examined in more detail to produce insightful results. 

Because of the similarity in the connecting pattern between neurons to the structure of the animal visual cortex, convolutional networks were inspired by biological processes. Only in a small area of the visual field known as the receptive field do individual cortical neurons react to stimuli. Different neurons' receptive areas partially overlap to encompass the whole visual field. 

How do Convolutional Neural Networks work?  

Convolutional Neural Network Architecture: For CNN to function, a picture must first be retrieved, its various items given a weight, and then those things must be distinguished from one another. Compared to other deep learning algorithms, CNN requires very little pre-processing data. CNN uses simple techniques to train its classifiers, which allows it to be sufficiently intelligent to learn the properties of the target object. This is one of its primary features. 

Understanding of CNN with animal photo examples

CNN algorithm is based on various modules that are structured in a specific workflow that are listed as follows: 

  • Convolution Layer (Kernel) 

  • Pooling Layer 

  • Classification — Fully Connected Layer 


Working of CNN algorithm

Convolutional Layer (Kernal) 

Most of the computation in a CNN takes place in the convolutional layer, which is its fundamental building component. Three things are needed: a feature map, a filter, and input data. For the time being, let's assume that the input will be a color picture made up of a three-dimensional matrix of pixels. This means that the RGB values of an image will match the three dimensions of the input—height, width, and depth.  

Additionally, we have a feature detector—also referred to as a kernel or filter—that will scan the image's receptive fields to determine whether the feature is there. We call this process convolution. 

However, before the neural network starts training, three hyperparameters that impact the output's volume size must be established. Among them are: 

1. The output's depth is influenced by the number of filters. For instance, obtaining three different feature maps with three different filters would result in a depth of three.  

2. The kernel's stride is the number of pixels it travels over the input matrix. A bigger stride results in a reduced output, albeit stride values of two or higher are uncommon. 

3. When the filters do not match the input image, zero-padding is typically utilized. This results in an output larger or equal by setting all elements outside the input matrix to zero. 


Impact of 3 hyperparameters in CNN

A two-dimensional (2-D) array of weights that represents a portion of the image serves as the feature detector. The filter size is usually a 3x3 matrix, however, they can vary in size; this also establishes the size of the receptive field. The dot product between the input pixels and the filter is determined after the filter has been applied to a section of the image. 

 An output array is then supplied with this dot product. The filter then moves one step forward and backward until the kernel has scanned the entire image. A feature map, activation map, or convolved feature is the result of the series of dot products created from the input and filter. 

Example of 2-D input and output arrays in CNN

Depending on the desired result, these layers seek to either increase or decrease the dimensionality of the image included in the original input image, or they may choose to leave it unchanged. When there is no need to alter the matrix's dimensions, proper padding is applied; nevertheless, the same padding is applied when the picture is convoluted to fit various matrix dimensions. 

Pooling Layer 

Down sampling, or pooling layers, does dimensionality reduction by lowering the number of parameters in the input. While the pooling operation sweeps a filter across the entire input, it differs from the convolutional layer in that the filter is weightless. Rather, the values in the receptive field are subjected to an aggregation function by the kernel, which then fills the output array. Two primary categories of pooling exist: 

  1. Max Pooling: The pixel with the highest value is chosen by the filter as it passes over the input and is sent to the output array. In addition, this method is typically applied more frequently than ordinary pooling. Max Pooling's other feature, which involves eliminating activations that comprise noisy activation, likewise suppresses noise. 

  1. Average Pooling: The average value in the receptive field is determined by the filter as it passes over the input and is sent to the output array. All that the Average Pooling does is apply dimensionality reduction to the noise-suppression process. 

The Convolutional Neural Network's "i-th layer" is made up of the Pooling layer and the Convolutional Layer combined. Depending entirely on the complexities of the image, layer counts may increase to achieve the goal of capturing the finer details, but they also require greater processing power. Upon examining the process information provided above, we can run the model and comprehend the features with ease. Also, to classify the data, we are going to obtain the output and use it as an input for the standard neural network. 

Classification - Fully Connected Layer 

Every neuron in one layer communicates with every other layer's neuron through fully connected layers. It is equivalent to an MLP, or multilayer perceptron neural network. To categorize the photos, the flattened matrix passes through a fully linked layer. 

Using the features that were retrieved from the earlier layers and their various filters, this layer classifies the data. FC layers typically utilize a softmax activation function to categorize inputs adequately, producing a probability from 0 to 1, whereas convolutional and pooling layers typically use ReLu functions. 

Types of Convolutional Neural Networks 

Types of Convolutional Neural Networks

      Limitation of Convolutional Neural Networks (CNN)

      To train efficiently, they need a lot of labeled data, which can be expensive and time-consuming to collect and annotate. 

      They can get overfitted and fail to generalize to new and different data because they are prone to memorizing the noise and minutiae of the training data. To prevent overfitting, various regularization techniques, such as dropout, batch normalization, and data augmentation, must be applied, which can increase the complexity and computational cost of the network. 

      CNNs also have the drawback of being viewed as "black boxes," which makes them challenging to understand and comprehend. This can make it difficult to debug, validate, and have faith in the judgments made by the network, particularly in delicate and important fields like law, security, and healthcare. 

      Convolutional Neural Network (CNN) Applications

      • Understanding Face Recognition   
      • Image Identification  
      • Analyzing Videos   
      • Natural Language Processing.  
      • Identification of Anomalies   
      • Medicine Discovery   
      • Games  
      • Time Series Forecasting  


      Convolutional neural networks (CNNs) are a particular kind of artificial neural network designed for image processing applications, to sum up. CNNs make use of special layers such as the convolutional layer and the pooling layer, in contrast to fully connected neural networks, which are computationally demanding and less effective for picture tasks. By applying filters to picture pixels, the convolutional layer creates a convolved feature map that captures features. In the meantime, the feature map's size is decreased by the pooling layer using methods like Average Pooling, which calculates the average value, and Max Pooling, which chooses the largest value. Finally, the flattened feature map feeds into a fully linked layer for classification after passing through several stages of processing. CNNs are hence feedforward networks skilled at effectively filtering and interpreting geographical data.