IMAGE CLASSIFICATION USING ARTIFICIAL NEURAL NETWORKS: AN EXPERIMENTAL STUDY ON COREL DATABASE

In this paper high-level image classes are inferred from low-level image features like color and shape features with the help of artificial neural network. Back propagation neural network algorithm is used for integrating knowledge from low-level image features and classify the images into high level concepts / semantic classes. The classifier is evaluated on a database of 1000 images from COREL database. The experimental results show that the accuracy using back propagation neural network algorithm to classify COREL images ranges between 80.5% to 88.6%.