Can texture and image content retrieval methods match human perception?

2002 ◽  
Author(s):  
J.S. Payne ◽  
T.J. Stonbam
Keyword(s):  
Human Perception ◽  
Image Content ◽  
Content Retrieval

scholarly journals Contrast invariant tuning in human perception of image content

2019 ◽  
Author(s):  
Ingo Fruend ◽  
Jaykishan Patel ◽  
Elee D. Stalker
Keyword(s):  
Visual Processing ◽  
Discrimination Performance ◽  
Human Perception ◽  
Image Features ◽  
Image Contrast ◽  
Visual Features ◽  
Image Content ◽  
Generative Adversarial Network ◽  
Adversarial Network ◽  
Contrast Discrimination

AbstractHigher levels of visual processing are progressively more invariant to low-level visual factors such as contrast. Although this invariance trend has been well documented for simple stimuli like gratings and lines, it is difficult to characterize such invariances in images with naturalistic complexity. Here, we use a generative image model based on a hierarchy of learned visual features—a Generative Adversarial Network—to constrain image manipulations to remain within the vicinity of the manifold of natural images. This allows us to quantitatively characterize visual discrimination behaviour for naturalistically complex, non-linear image manipulations. We find that human tuning to such manipulations has a factorial structure. The first factor governs image contrast with discrimination thresholds following a power law with an exponent between 0.5 and 0.6, similar to contrast discrimination performance for simpler stimuli. A second factor governs image content with approximately constant discrimination thresholds throughout the range of images studied. These results support the idea that human perception factors out image contrast relatively early on, allowing later stages of processing to extract higher level image features in a stable and robust way.

A Secondary Pretreatment Method of Particled Graph

2013 ◽  
Vol 760-762 ◽  
pp. 1394-1397
Author(s):  
Ming Xin Li ◽  
Xiong Fei Li
Keyword(s):  
Data Mining ◽  
Image Processing ◽  
Data Storage ◽  
Structured Data ◽  
New Method ◽  
Image Content ◽  
Storage Model ◽  
Pretreatment Method ◽  
The Core ◽  
Content Retrieval

According to the coordinates of the particled graph, the paper presents a secondary pretreatment method which is invariant to translation, scaling and rotation. Having the advantages of efficient and accurate, the new method is significant to image processing. As the core of the pretreatment, orientation pretreatment is described in detail. In combination with the semi-structured data storage model, the new pretreatment method can help to achieve image content retrieval and data mining of a deeper level.

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