Non-Linear Optimization and Discrete Convolutional Operators in Deep Learning Neural Architectures
TL;DR
This research uses Convolutional Neural Network (CNN) to build the models, decreasing the dimensionality of images without losing any content by using a built-in convolutional layer.
Abstract
Image classification is a well-known problem in image processing, computer vision, and machine learning. We investigate picture categorization using deep learning in this research. Nowadays bird watching is becoming a common hobby for everyone, but more than 10,000 species are part of the ecosystem, which causes difficulty in identification and prediction. Additionally, the birds may appear in different scenarios and also in different shapes, sizes, and colors. So, we use Convolutional Neural Network (CNN) to build the models, decreasing the dimensionality of images without losing any content by using a built-in convolutional layer. This will identify the input given by the user and starts the image processing and then compares it with a trained model and predicts the species of the bird. The model will return the output with the predicted probability of the species. If the user-given image is not available in the dataset, the model automatically adds it to the dataset which will be useful in building the dataset. This model helps in the classification and recognition of the birds. Key words: Deep Learning (DL) , Convolution Neural Network(CNN) ,Image Classification. Keywords—component, formatting, style, styling, insert