Knowledge Based Image Annotation Refinement

Abstract

Recently, images on the Web and personal computers are prevalent around the human's life. To retrieve effectively these images, there are many (Automatic Image Annotation) AIA algorithms. However, it still suffers from low-level accuracy since it couldn't overcome the semantic-gap between low-level features (`color', `texture' and `shape') and high-level semantic meanings (e.g., `sky', `beach'). Namely, AIA techniques annotates images with many noisy keywords. In this paper, we propose a novel approach that augments the classical model with generic knowledge-based, WordNet. Our novel approach strives to prune irrelevant keywords by the usage of WordNet. To identify irrelevant keywords, we investigate various semantic similarity measures between keywords and finally fuse outcomes of all these measures together to make a final decision using Dempster-Shafer evidence combination. Furthermore, We can re-formulate the removal of erroneous keywords from image annotation problem into graph-partitioning problem, which is weighted MAX-CUT problem. It is possible that we have too many candidate keywords for web-images. Hence, we need to have deterministic polynomial time algorithm for MAX-CUT problem. We show that finding optimal solution for removing noisy keywords in the graph is NP-Complete problem and propose a new methodology for Knowledge Based Image Annotation Refinement (KBIAR) using a deterministic polynomial time algorithm, namely, randomized approximation graph algorithm. Finally, we demonstrate the superiority of this algorithm over traditional one including the most recent work for a benchmark dataset.