Thousands of images are generated every day, which implies the necessity to classify and access them by an easy and faster way. The main objective of classification is to identify the features occurring in the image. Neural networks (NNs), inspired by biological neural system, are a family of supervised machine learning algorithms that allow machine to learn from training instances as mathematical models. NNs have been widely applied in the fields of classification, optimization, and control theory. This work compares the classification of images using Convolutional Deep Neural Network approaches.
Thousands of images are generated every day, which implies the necessity to classify and access them by an easy and faster way. The main objective of classification is to identify the features occurring in the image. Neural networks (NNs), inspired by biological neural system, are a family of supervised machine learning algorithms that allow machine to learn from training instances as mathematical models. NNs have been widely applied in the fields of classification, optimization, and control theory. This work compares the classification of images using Convolutional Deep Neural Network approaches.