Question

how to find specific patterns in different locations in images, what technique in CNN ? And how it work with complex images?

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Answer:

Finding specific patterns in different locations in images and techniques used in CNN. And working with complex images:

Unlike a fully connected neural network, in a Convolutional Neural Network (CNN) the neurons in one layer don’t connect to all the neurons in the next layer. Rather, a convolutional neural network uses a three-dimensional structure, where each set of neurons analyzes a specific region or “feature” of the image. CNNs filters connections by proximity (pixels are only analyzed in relation to pixels nearby), making the training process computationally achievable. In a CNN each group of neurons focuses on one part of the image. For example, in a cat image, one group of neurons might identify the head, another the body, another the tail, etc. There may be several stages of segmentation in which the neural network image recognition algorithm analyzes smaller parts of the images, for example, within the head, the cat’s nose, whiskers, ears, etc. The final output is a vector of probabilities, which predicts, for each feature in the image, how likely it is to belong to a class or category.

Effectiveness and Limitations in Convolution Neural Networks:

A CNN architecture makes it possible to predict objects and faces in images using industry benchmark datasets with up to 95% accuracy, greater than human capabilities which stand at 94% accuracy. Even so, convolutional neural networks have their limitations:

• Require high processing power. Models are typically trained on high-cost machines with specialized Graphical Processing Units (GPUs).

• Can fail when images are rotated or tilted, or when an image has the features of the desired object, but not in the correct order or position, for example, a face with the nose and mouth switched around. A new architecture called CAPSNet has emerged to address this limitation.

Image Recognition Applications:

Implementations of image recognition include security and surveillance, face recognition, visual geolocation, gesture recognition, object recognition, medical image analysis, driver assistance, and image tagging and organization in websites or large databases. Image recognition has entered the mainstream. Face, photo, and video frame recognition is used in production by Facebook, Google, Youtube, and many other high profile consumer applications. Toolkits and cloud services have emerged which can help smaller players integrate image recognition into their websites or applications.