Title
Computational versus psychophysical bottom-up image saliency: A comparative evaluation study
Author
Toet, A.
Publication year
2011
Abstract
The predictions of 13 computational bottom-up saliency models and a newly introduced Multiscale Contrast Conspicuity (MCC) metric are compared with human visual conspicuity measurements. The agreement between human visual conspicuity estimates and model saliency predictions is quantified through their rank order correlation. The maximum of the computational saliency value over the target support area correlates most strongly with visual conspicuity for 12 of the 13 models. A simple multiscale contrast model and the MCC metric both yield the largest correlation with human visual target conspicuity ({}0.84). Local image saliency largely determines human visual inspection and interpretation of static and dynamic scenes. Computational saliency models therefore have a wide range of important applications, like adaptive content delivery, region-of-interest- based image compression, video summarization, progressive image transmission, image segmentation, image quality assessment, object recognition, and content-aware image scaling. However, current bottom-up saliency models do not incorporate important visual effects like crowding and lateral interaction. Additional knowledge about the exact nature of the interactions between the mechanisms mediating human visual saliency is required to develop these models further. The MCC metric and its associated psychophysical saliency measurement procedure are useful tools to systematically investigate the relative contribution of different feature dimensions to overall visual target saliency. © 2011 IEEE.
Subject
Human
PCS - Perceptual and Cognitive Systems
BSS - Behavioural and Societal Sciences
image analysis
Saliency
visual search
Adaptive content
Additional knowledge
Comparative evaluations
Conspicuity
Content-aware
Feature dimensions
Human visual
Human visual inspection
Image quality assessment
Image scaling
Lateral interactions
Measurement procedures
Multiscale contrast
Progressive image transmission
Psychophysical
Rank order
Region of interest
Relative contribution
Saliency
Static and dynamic
Target support
Video summarization
Visual effects
Visual search
Visual targets
Digital image storage
Forecasting
Image analysis
Image communication systems
Image compression
Image quality
Measurements
Object recognition
Visualization
Image segmentation
To reference this document use:
http://resolver.tudelft.nl/uuid:434b0bad-479b-42e7-a937-1aaaaa1150b8
DOI
https://doi.org/10.1109/tpami.2011.53
TNO identifier
436601
ISSN
0162-8828
Source
IEEE Transactions on Pattern Analysis and Machine Intelligence, 33 (11), 2131-2146
Article number
No.: 5740916
Document type
article