Abass A. Olaode¹, Golshah Naghdy¹ and Catherine A. Todd², ¹University of Wollongong, Australia and ²University of Wollongong in Dubai, UAE

The determination of Region-of-Interest has been recognised as an important means by which unimportant image content can be identified and excluded during image compression or image modelling, however existing Region-of-Interest detection methods are computationally expensive thus are mostly unsuitable for managing large number of images and the compression of images especially for real-time video applications. This paper therefore proposes an unsupervised algorithm that takes advantage of the high computation speed being offered by Speeded-Up Robust Features (SURF) and Features from Accelerated Segment Test (FAST) to achieve fast and efficient Region-of-Interest detection.

Region of Interest, Image segmentation, SURF, FAST, Texture description, PLSA, BOV, K-means clustering, unsupervised image classification.