Image Modification Based on a Visual Saliency Map for Guiding Visual Attention

Abstract

SUMMARYIt is commonly believed that improved interaction between humans and electronic device, it is effective to draw the viewer's attention to a particular object. Augmented reality (AR) applications can call attention to real objects by overlaying highlight effects or visual stimuli (such as arrows) on a physical scene. Sometimes, more subtle effects would be desirable, in which case it would be necessary to smoothly and naturally guide the user's gaze without external stimuli. Here, a novel image modification method is proposed for directing a viewer's gaze to specific regions of interest. The proposed method uses saliency analysis and color modulation to create modified images in which the region of interest is the most salient region in the entire image. The proposed saliency map model that is used during saliency analysis reduces computational costs and improves the naturalness of the image using the LAB color space and simplified normalization. During color modulation, the modulation value of each LAB component is determined in order to consider the relationship between the LAB components and the saliency value. With the image obtained in this manner, the viewer's attention is smoothly attracted to a specific region very naturally. Gaze measurements as well as a subjective experiments were conducted to prove the effectiveness of the proposed method. These results show that a viewer's visual attention is indeed attracted toward the specified region without any sense of discomfort or disruption when the proposed method is used.

Image Modification Based on Spatial Frequency Components for Visual Attention Retargeting

“…In addition, when other objects with high saliency exist in an original image, it is difficult to effectively guide human gaze to the modified ROI. Takimoto et al [9] used a novel saliency analysis method and color modulation to create modified images in which the ROI is the most salient region in the entire image. The saliency map model used in their saliency analysis reduces the computational cost and improves the naturalness of the image by using an L*A*B* color space and simplified normalization.…”

“…The above-mentioned methods [7]- [9] are strictly based on a visual saliency computation model for bottomup attention; thus, they are able to guide human attention effectively. Although state-of-the-art color-based method [9] achieves attention retargeting without degradation of visibility, it is not necessarily effective for all images.…”

“…Although state-of-the-art color-based method [9] achieves attention retargeting without degradation of visibility, it is not necessarily effective for all images. In addition, both color-and orientation-based methods can be applied an image simultaneously because the characteristics used by each method are different.…”

“…These methods adaptively modify the RGB color components by reverse engineering a typical visual saliency map model. Takimoto et al [9] proposed a modification method that iteratively modulates the lightness and color components on the basis of a novel saliency map using an L*a*b* color space. This method requires significantly less computational time than other methods and produces images that are considered much more natural.…”

“…This method requires significantly less computational time than other methods and produces images that are considered much more natural. The abovementioned methods [7]- [9] are able to guide human attention effectively because they are strictly based on a visual saliency computation model for bottom-up attention, i.e., they are categorized under "feedback from saliency computation. "…”