Targeted Visual Content Recognition Using Multi-Layer Perceptron Neural Network

Visual Content Recognition has become an attractive research oriented field of computer vision and machine learning for the last few decades. The focus of this work is monument recognition. Imagesof significant locations captured and maintainedas data bases can be used by the travelers before visiting the places. They can use images of a famous building to know the description of the building. In all these applications, the visual content recognition plays a key role. Humans can learn the contents of the images and quickly identify them by seeing again. In this paper we present a constructive training algorithm for Multi-Layer Perceptron Neural Network (MLPNN) applied to a set of targeted object recognition applications. The target set consists of famous monuments in India for travel guide applications. The training data set (TDS) consists 3000 images. The Gist features are extracted for the images. These are given to the neural network during training phase.The mean square error (MSE) on the training data is computed and used as metric to adjust the weights of the neural network,using back propagation algorithm. In the constructive learning, if the MSE is less than a predefined value, the number of hidden neurons is increased. Input patterns are trained incrementally until all patterns of TDS are presented and learned. The parameters or weights obtained during the training phase are used in the testing phase, in which new untrained images are given to the neural network for recognition. If the test image is recognized, the details of the image will also be displayed. The performance accuracy of this method is found to be 95%