A Novel Technique to Image Annotation using Neural Network

Automatic annotation of digital pictures is a key technology for managing and retrieving images from large image collection. Traditional image semantics extraction and representation schemes were commonly divided into two categories, namely visual features and text annotations. However, visual feature scheme are difficult to extract and are often semantically inconsistent. On the other hand, the image semantics can be well represented by text annotations. It is also easier to retrieve images according to their annotations. Traditional image annotation techniques are time-consuming and requiring lots of human effort. In this paper we propose Neural Network based a novel approach to the problem of image annotation. These approaches are applied to the Image data set. Our main work is focused on the image annotation by using multilayer perceptron, which exhibits a clear-cut idea on application of multilayer perceptron with special features. MLP Algorithm helps us to discover the concealed relations between image data and annotation data, and annotate image according to such relations. By using this algorithm we can save more memory space, and in case of web applications, transferring of images and download should be fast. This paper reviews 50 image annotation systems using supervised machine learning Techniques to annotate images for image retrieval. Results obtained show that the multi layer perceptron Neural Network classifier outperforms conventional DST Technique.