Abstract
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.
| Original language | English |
|---|---|
| Article number | 7374748 |
| Pages (from-to) | 2564-2578 |
| Number of pages | 15 |
| Journal | IEEE Transactions on Visualization and Computer Graphics |
| Volume | 22 |
| Issue number | 12 |
| DOIs | |
| Publication status | Published - 2016 Dec 1 |
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All Science Journal Classification (ASJC) codes
- Software
- Signal Processing
- Computer Vision and Pattern Recognition
- Computer Graphics and Computer-Aided Design
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Measuring and Predicting Visual Importance of Similar Objects. / Kong, Yan; Dong, Weiming; Mei, Xing; Ma, Chongyang; Lee, Tong Yee; Lyu, Siwei; Huang, Feiyue; Zhang, Xiaopeng.
In: IEEE Transactions on Visualization and Computer Graphics, Vol. 22, No. 12, 7374748, 01.12.2016, p. 2564-2578.Research output: Contribution to journal › Article
TY - JOUR
T1 - Measuring and Predicting Visual Importance of Similar Objects
AU - Kong, Yan
AU - Dong, Weiming
AU - Mei, Xing
AU - Ma, Chongyang
AU - Lee, Tong Yee
AU - Lyu, Siwei
AU - Huang, Feiyue
AU - Zhang, Xiaopeng
PY - 2016/12/1
Y1 - 2016/12/1
N2 - 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.
AB - 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.
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UR - http://www.scopus.com/inward/citedby.url?scp=84993965231&partnerID=8YFLogxK
U2 - 10.1109/TVCG.2016.2515614
DO - 10.1109/TVCG.2016.2515614
M3 - Article
AN - SCOPUS:84993965231
VL - 22
SP - 2564
EP - 2578
JO - IEEE Transactions on Visualization and Computer Graphics
JF - IEEE Transactions on Visualization and Computer Graphics
SN - 1077-2626
IS - 12
M1 - 7374748
ER -