An automatic large image browsing technique on small displays

Abstract

Visual information presentations on small displays are being increased as the use of mobile communication is increasing day by day. Digital image is one of the most popular forms of visual information which is easily shared and accessible. However, a challenge is to provide a better user experience on heterogeneous small display sizes. In this thesis, a novel detail and efficient algorithm is proposed to browse large images by automatic panning and zooming on Region-of-Interests (ROIs) of image for small display users. Images are classified into two different categories. First category images consists of images which contains focused human face and the other category images which do not contain human face. ROIs of image are extracted by using top-down and bottom-up approaches. For images which contain human faces, we extract face regions and mark the regions as ROIs. The other images are segmented based on color similarity using an efficient segmentation algorithm. For each segmented region, saliency is calculated by using three features (contrast, color saliency and size) of the regions. ROIs are browsed using different intuitive and study based strategies. Before start browsing, regions are adapted to the display aspect ratio. Our proposed system is evaluated by subjective test and evaluation results indicate that the proposed system is an effective large image displaying technique on small displays.