|
|
| |
| Adoptive Techniques Used For Image Recognition |
A.Prathyusha, K.Joshna
|
Abstract:
Image Recognition is the ability of software to identify objects, places, people, writing and actions in images. Image recognition is one of the most used technique for machine-based task, image content search and for guiding autonomous robots. while for humans and animals recognize objects with very easy. Deep machine learning is one of the software for image recognition. Performance is best on convolutional neural net processors. Deep learning-based image recognition has become "superhuman", producing more accurate results than human contestants. Deep learning technology uses a multilayer structure to analyze and deal with image features. In order to apply deep learning to the field of image recognition, the basic principle, training process, and model structure of deep belief networks (DBNs) in deep learning are analyzed. DBNs are multilayer generative models where each layer is connected but not between units within each layer. Convolutional Neural Networks (CNN) are multi-layered neural networks that are specialized in pattern recognition tasks, variants of Multi-Layer Perceptrons (MLPs).CNN is are hierarchical neural networks relying on convolution interlaced with pooling layers, for automatic feature extraction and a series of fully connected layers that will perform the final classification.
|
|
|
| | |