- **Parameters Sharing**: a feature that is detected as useful in part of the image is probably useful in other parts as well.
- **Sparsity of Connections**: in each layer, each output value depends only on a small number of inputs, rather than considering all the pixel inputs.

Advantages of Using Convolutional Neural Networks for Image Recognition

Edge detection is a fundamental image processing application of convolutional layers. It involves locating the boundaries of an object within an image by finding the precise locations of pixel changes. By performing a cross-correlation or convolution operation with a specifically constructed filter matrix, or kernel, the layer can successfully detect distinct kinds of boundaries, such as vertical or horizontal edges.

Object Edge Detection Using Convolution

The accuracy of object recognition has dramatically increased due to deep learning architectures. For example, on the ImageNet benchmark, the top-five classification error rate plummeted from 28% in 2010 to 2.25% by 2017. Beyond benchmark datasets, this improved visual perception enables complex real-world applications such as diagnosing skin cancer from medical images and identifying birdsong.

Object Recognition Benchmark Improvements

One of the most popular introductory applications of CNN is image recognition, specifically sorting through images and classifying them as either cat or not cat.

University of Michigan - Ann Arbor

Google

CNNs are currently being applied to several applications, including:

 - Computer vision—medical image analysis, image recognition, face detection and video classification
 - Natural Language Processing (NLP)—semantic parsing, sentence modeling and search query retrieval
 - Drug discovery—the discovery of chemical features and prediction of medicinal benefits

Applications of Convolutional Neural Networks

Computer task is to predict the binary label of an image. 

For example, is this an image of cat or not (y=0 or y=1)

Image Recognition (Classification) in Computer Vision

Machine Learning Yearning, a free ebook from Andrew Ng, teaches you how to structure Machine Learning projects.
https://www.deeplearning.ai/machine-learning-yearning/

Note: The content of the book is aligned with the Coursera Deeplearning.ai specialization.  https://www.deeplearning.ai/deep-learning-specialization/ 

Machine Learning Yearning (Deeplearning.ai)

Using Convolutional Neural Network for Image Recognition

"Large-scale Video Classification with Convolutional Neural Networks" is one of the first papers that have mentioned CNN for video classification. Two main questions addressed by this paper are 

 - "what temporal connectivity pattern in a CNN architecture is best at taking advantage of local motion information present in the video?"

- "How does the additional motion information influence the predictions
of a CNN and how much does it improve performance overall? "

Convolutional Neural Networks for Video classification

https://thenextweb.com/artificial-intelligence/2018/07/18/a-beginners-guide-to-ai-computer-vision-and-image-recognition/

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