Research Area: Machine Learning
Images and graphics are among the most important media formats for human communication and they provide a rich amount of information for people to understand the world. With the rapid development of digital imaging techniques and Internet, more and more images are available to public. Consequently, there is an increasingly high demand for effective and efficient image indexing and retrieval methods. However with the widely spread digital imaging devices, textual annotation of images be-comes impractical and inefficient for image representation and retrieval.To diminish the reliance on the textual annotations and associated meta-data for image search, the content based image retrieval (CBIR) has be-come one of the most popular topics in the field of computer vision and pattern recognition. In CBIR, the image representations are generated through the visual clues like color, texture, or shape of objects; and certain machine learning algorithms are applied to understand the image semantics for meaningful image retrieval. However, despite the great deal of research work, the image retrieval performance of the CBIR systems is not satisfactory due to the existent semantic gap between the low level image representations and high-level visual concepts.To bridge this gap to some extent, three major issues in the active field of CBIR are investigated in this thesis, that are: consistency enhancement during the semantic association, improvement in the relevance feedback(RF) mechanism, and generation of a stable semantic classifier.
Name of the Researcher: Syed Aun Irtaza
Name of the Supervisor(s): Dr. M. Arfan Jaffar
Year of Completion: 2015
University: National University of Computer & Emerging Sciences Islamabad
Thesis Link: Home Page Url
