A semantic learning for content-based image retrieval using analytical hierarchy process

Abstract

In this paper, a new semantic learning method for content-based image retrieval using the analytic hierarchical process (AHP) is proposed. AHP proposed by Satty used a systematical way to solve multi-criteria preference problems involving qualitative data and was widely applied to a great diversity of areas. In general, the interpretations of an image are multiple and hard to describe in terms of low-level features due to the lack of a complete image understanding model. The AHP provides a good way to evaluate the fitness of a semantic description used to interpret an image. According to a predefined concept hierarchy, a semantic vector, consisting of the fitness values of semantic descriptions of a given image, is used to represent the semantic content of the image. Based on the semantic vectors, the database images are clustered. For each semantic cluster, the weightings of the low-level features (i.e. color, shape, and texture) used to represent the content of the images are calculated by analyzing the homogeneity of the class. In this paper, the values of weightings setting to the three low-level feature types are diverse in different semantic clusters for retrieval. The proposed semantic learning scheme provides a way to bridge the gap between the high-level semantic concept and the low-level features for content-based image retrieval. Experimental results show that the performance of the proposed method is excellent when compared with that of the traditional text-based semantic retrieval techniques and content-based image retrieval methods.