Retrieval of images from artistic repositories using a decision fusion framework.

Abstract

The large volumes of artistic visual data available to museums, art galleries, and online collections motivate the need for effective means to retrieve relevant information from such repositories. This paper proposes a decision making framework for content-based retrieval of art images based on a combination of low-level features. Traditionally, the similarity among two images has been calculated as a weighted distance between two feature vectors. This approach, however, may not be mathematically and computationally appropriate and does not provide enough flexibility in modeling user queries. This paper proposes a framework that generalizes a wide set of previous approaches to similarity calculation including the weighted distance approach. In this framework, image similarities are obtained through a decision making process based on low-level feature distances using fuzzy theory. The analysis and results of this paper indicate that the aggregation technique presented here provides an effective, general, and flexible tool for similarity calculation based on the combination of individual descriptors and features.