A Dynamic Multi Label Image Classification based on Recurrent Neural Networks

Abstract

The traditional technique used for image recognition has complexity in the construction of algorithm and the training speed for the system to analyze algorithm is also too high so, the computation of the algorithm becomes very difficult in order to overcome this lack of computation. The proposed system is very efficient in both training as well as the computation speed required for the image recognition. Since, the proposed system uses the traditional LSTM algorithm which is one of the backbone factors of the RNN technique as it predicts the input on the basis of sequential analysis as it uses tanh function in order to remove the negative values of the matrix and it also predicts and removes the error in the input with the use of differential formulas in order to formulate the outcome desired for the image to be recognized. Because of this sequential analysis of the data increases the future scope of image recognition in the field of deep learning, and also because of its efficient use of the algorithm in comparison with the existing algorithm like ANN, CNN.