Description
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The progress in digitalisation techniques gave the impetus for the development of Content-Based Image Retrieval Systems (CBIRS) that use automatically extracted features to find images in large repositories. Up to now, the benefit from the incorporation of knowledge about the human visual system in the CBIRS design and implementation process has been mostly overlooked. In this context, the author developed a new eye-tracking based retrieval technique, called Vision-Based Image Retrieval (VBIR), where users' eye movements are used online to dynamically adjust the weights for locally calculated image features. Thus, the search can be directed towards information of increasing relevance, leading not only to better retrieval performances, but also to higher correlation of the systems' retrieval results with human measures of similarity. Furthermore, this book is about other central aspects for image retrieval: the estimation of the optimal feature weights, the evaluation of the chosen image features and the design of computer models. This book addresses researchers and developers who are interested in the design of more natural and intuitive image retrieval techniques.
(Author portrait)
Essig, Kai Kai Essig graduated in Computer Science and Chemistry (M.Sc.) and received a Ph.D. in Computer Science from Bielefeld University, Germany. He is currently a postdoctoral researcher in the Excellence Cluster 277 Cognitive Interaction Technology (CITEC) at Bielefeld University.
