Obtaining Description for Simple Images using Surface Realization Techniques and Natural Language Processing

This paper aims at developing a simple mechanism to deduce corpora pertaining to an image through various computer vision and natural language processing techniques. The output of the vision detection is combined with the sentence formation approach to get the visual content in textual form. Vision detections are smoothed using a number of approaches to prune undesired combination of words that are semantically incorrect. Descriptions are generated based on syntactic trees and Markov Chains and compared for human likeness based on survey. The results of the survey indicate that the descriptions generated with the help of Markov Chains sound more human like. These generated descriptions can be indexed in lucene and image search can be made more efficient bridging the semantic gap.