Modeling Visual Saliency in Images and Videos

Applications In Multimedia

The two issues that limit the even more widespread use of multimedia content than in the present situation are their huge capacity and their high complexity. The use of visual saliency is a natural way to overcome these limitations by selecting relevant visual information and processing only the visual attention region. This mechanism can simultaneously improve the efficiency and robustness of various multimedia applications. In multimedia adaptation, images can be adapted (Chen et al., 2003) and browsed (Xie et al., 2006) or video sequences can be progressively transmitted for display (Hu et al., 2004) on small screen devices by preserving salient content. For Content-based Image Retrieval (CBIR) systems, detecting salient regions can improve the system performance by reducing the influence of cluttered background (Bamidele et al., 2004; Wang et al., 2004). Modeling visual saliency can also facilitate visual tracking due to the common issues that they address: salient content can be used to initialize, detect as well as recover tracking target (Brajovic & Kanade, 1998; Toyama & Hager, 1999; Yang et al.. 2007). Recently, media retargeting techniques (Shamir & Avidan, 2009; Wolf et al., 2007) have been reported that rely on visual saliency modeling to indicate important information that needs to be preserved. As digital cameras become ubiquitous, their technology aims to help the amateur photographer to capture pictures that are aesthetically much superior than before. Face detection is already available in many such digital cameras. Clearly, there is a role for visual attention that can improve the performance of this task, as also in others such as automatically focusing on a certain area of the scene that is visually salient and in automatic zooming into salient regions.