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Recent research on computational modeling of visual attention has demonstrated that a bottom-up approach to identifying salient regions within an image can be applied to diverse and practical problems for which conventional machine vision techniques have not succeeded in producing robust solutions. This paper proposes a new method for extracting regions of interest (ROIs) from images using models of visual attention. It is presented in the context of improving content-based image retrieval (CBIR) solutions by implementing a biologically-motivated, unsupervised technique of grouping together images whose salient ROIs are perceptually similar. In this paper we focus on the process of extracting the salient regions of an image. The excellent results obtained with the proposed method have demonstrated that the ROIs of the images can be independently indexed for comparison against other regions on the basis of similarity for use in a CBIR solution.