Publication of the week: H. Du, H. Al-Jubouri & H. Sellahewa

H. Du, H. Al-Jubouri & H. Sellahewa, “Effectiveness of image features and similarity measures in cluster-based approaches for content-based image retrieval”, Proc. Mobile Multimedia/Image Processing, Security, and Applications (SPIE 9120, 912008, May 2014); doi: 10.1117/12.2057721.

Content-based image retrieval is an automatic process of retrieving images according to image visual contents instead of textual annotations. It has many areas of application from automatic image annotation and archive, image classification and categorisation to homeland security and law enforcement. The key issues affecting the performance of such retrieval systems include sensible image features that can effectively capture the right amount of visual contents and suitable similarity measures to find similar and relevant images ranked in a meaningful order. Many different approaches, methods and techniques have been developed as a result of very intensive research in the past two decades. Among many existing approaches is a cluster-based approach where clustering methods are used to group local feature descriptors into homogeneous regions, and search is conducted by comparing the regions of the query image against those of the stored images. This paper serves as a review of works in this area. The paper first summarises the existing work reported in the literature and then presents the authors’ own investigations in this field. The paper highlights not only achievements made by recent research but also challenges and difficulties still remaining in this area.

Hongbo Du and Harin Sellahewa are Senior Lecturers in Computer Science at Buckingham, and Hanan Al-Jubouri is a research student.