Image retrieval using visual attention

Author: Liam M. Mayron Title: Image retrieval using visual attention Institution: Florida Atlantic University Dissertation Advisor: Dr. Oge Marques Degree: Doctor of Philosophy Year: 2008 The retrieval of digital images is hindered by the semantic gap. The semantic gap is the disparity between a user’s high-level interpretation of an image and the information that can be extracted from an image’s physical properties. Contentbased image retrieval systems are particularly vulnerable to the semantic gap due to their reliance on low-level visual features for describing image content. The semantic gap can be narrowed by including high-level, user-generated information. Highlevel descriptions of images are more capable of capturing the semantic meaning of image content, but it is not always practical to collect this information. Thus, both content-based and human-generated information is considered in this work. A content-based method of retrieving images using a computational model of visual attention was proposed, implemented, and evaluated. This work is based on a study of contemporary research in the field of vision science, particularly computational models of bottom-up visual attention. The use of computational models