Unsupervised Learning of Models for Recognition
ECCV '00 Proceedings of the 6th European Conference on Computer Vision-Part I
Scene-Centered Description from Spatial Envelope Properties
BMCV '02 Proceedings of the Second International Workshop on Biologically Motivated Computer Vision
Labeling images with a computer game
Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
Distinctive Image Features from Scale-Invariant Keypoints
International Journal of Computer Vision
Efficient Image Matching with Distributions of Local Invariant Features
CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Volume 2 - Volume 02
Discovering Objects and their Localization in Images
ICCV '05 Proceedings of the Tenth IEEE International Conference on Computer Vision (ICCV'05) Volume 1 - Volume 01
Beyond Bags of Features: Spatial Pyramid Matching for Recognizing Natural Scene Categories
CVPR '06 Proceedings of the 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - Volume 2
Using Multiple Segmentations to Discover Objects and their Extent in Image Collections
CVPR '06 Proceedings of the 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - Volume 2
2006 Special Issue: Modeling attention to salient proto-objects
Neural Networks
Making computers look the way we look: exploiting visual attention for image understanding
Proceedings of the international conference on Multimedia
An eye fixation database for saliency detection in images
ECCV'10 Proceedings of the 11th European conference on Computer vision: Part IV
Measuring and Predicting Object Importance
International Journal of Computer Vision
International Journal of Computer Vision
Learning the Relative Importance of Objects from Tagged Images for Retrieval and Cross-Modal Search
International Journal of Computer Vision
Multimedia Tools and Applications
| Hi-index | 0.00 |
We observe that everyday images contain dozens of objects, and that humans, in describing these images, give different priority to these objects. We argue that a goal of visual recognition is, therefore, not only to detect and classify objects but also to associate with each a level of priority which we call `importance'. We propose a definition of importance and show how this may be estimated reliably from data harvested from human observers. We conclude by showing that a first-order estimate of importance may be computed from a number of simple image region measurements and does not require access to image meaning.