Semantic Image Retrieval Using Analytical Hierarchy Process

Abstract

In this paper, a new semantic image retrieval method using the analytic hierarchical process (AHP) is proposed. AHP proposed by Satty used a systematically way to solve multi-criteria preference problem involving qualitative data and was widely applied to great diversity areas. The meanings of an image are multiple and heavily depend on the features used to interpret it. The AHP provides a good way to evaluate the fitness of a semantic description used to represent an image. A multi-dimensional vector, consists of the values of fitness of semantic descriptions about a given image, is used to represent the content of the image. Based on the vector representation, a similarity measurement between two semantic vectors in which one is from a query image and the other is from a database image is proposed in this study. Given a query image, the retrieved images are ranked according to their values of similarity. Comparing with the traditional text-based semantic retrieval techniques, the proposed method can display the retrievals according to the values of similarity. That is more similar images for a query image will be displayed first and users are easy to decide when they should stop the browsing. Furthermore, a relevance feedback mechanism is added to the system in order to further involve users in the retrieval loops. Experimental results show that the performance of the proposed method is excellent as compared to traditional text-based semantic retrieval techniques.