Large Scale Deep Network Architecture of CNN for Unconstraint Visual Activity Analytics

Abstract

Handling the issues of massive datasets for information retrieval, feature learning, is expected one of the most challenging problems in machine learning and computer vision research. The issues in this work, have been focused to maintain the data scalability problems for machine learning classifiers in social media activity analysis. The research highlights the machine learning performance techniques which can provide promising results against the large and unstructured complex data of social media activities. This work has been focused on the biologically inspired processing techniques by neural network and introduces the extension of this network to resolve the problems of complex data pertaining to human activity analysis. It is presented various architectures of CNN and several phases of visual data processing for detection and recognition problems. Some selected techniques are highlighted that create the interest for deep network learning in various domains of research under the consideration of complex data handlings. It has been introduced activation functions and sequence pooling methodology for fast training of convolutional network with massive data of unstructured human activity recognition. Overall, it is highlighted that fast training aspects of the network against large scale and complex data, can be improved by choosing activation function and pooling methodology at fully connected layers of the neural network. Moreover, the sounding techniques of deep learning and data analytics are highly applicable for human health, medicine, robotics, education and industrial applications.