Issue No. 12 - Dec. (2016 vol. 22)
Yan Kong , NLPR-LIAMA, Institute of Automation, Chinese Academy of Sciences, Beijing, China
Weiming Dong , NLPR-LIAMA, Institute of Automation, Chinese Academy of Sciences, Beijing, China
Xing Mei , NLPR-LIAMA, Institute of Automation, Chinese Academy of Sciences, Beijing, China
Chongyang Ma , University of Southern California, CA
Tong-Yee Lee , National Cheng-Kung University, Taiwan
Siwei Lyu , University at Albany-SUNY, Albany, NY
Feiyue Huang , Tencent, China
Xiaopeng Zhang , NLPR-LIAMA, Institute of Automation, Chinese Academy of Sciences, Beijing, China
Similar objects are ubiquitous and abundant in both natural and artificial scenes. Determining the visual importance of several similar objects in a complex photograph is a challenge for image understanding algorithms. This study aims to define the importance of similar objects in an image and to develop a method that can select the most important instances for an input image from multiple similar objects. This task is challenging because multiple objects must be compared without adequate semantic information. This challenge is addressed by building an image database and designing an interactive system to measure object importance from human observers. This ground truth is used to define a range of features related to the visual importance of similar objects. Then, these features are used in learning-to-rank and random forest to rank similar objects in an image. Importance predictions were validated on 5,922 objects. The most important objects can be identified automatically. The factors related to composition (e.g., size, location, and overlap) are particularly informative, although clarity and color contrast are also important. We demonstrate the usefulness of similar object importance on various applications, including image retargeting, image compression, image re-attentionizing, image admixture, and manipulation of blindness images.
Visualization, Image segmentation, Semantics, Electronic mail, Image color analysis, Animals, Object detection
Y. Kong et al., "Measuring and Predicting Visual Importance of Similar Objects," in IEEE Transactions on Visualization & Computer Graphics, vol. 22, no. 12, pp. 2564-2578, 2016.