Intuitive Large Image Database Browsing Using Perceptual Similarity Enriched by Crowds

2013 ◽  
pp. 169-176 ◽  
Author(s):  
Stefano Padilla ◽  
Fraser Halley ◽  
David A. Robb ◽  
Mike J. Chantler
Keyword(s):  
Image Database ◽  
Perceptual Similarity ◽  
Image Database Browsing

scholarly journals Intuitive Image Database Browsing to Support Image Annotation

Automatika ◽  
2012 ◽  
Vol 53 (4) ◽  
pp. 355-361 ◽  
Author(s):  
Gerald Schaefer ◽  
Matthew Stuttard
Keyword(s):  
Image Annotation ◽  
Image Database ◽  
Image Database Browsing

Task-Based User Evaluation of Content-Based Image Database Browsing Systems

2004 ◽  
pp. 234-242
Author(s):  
Timo Ojala ◽  
Markus Koskela ◽  
Esa Matinmikko ◽  
Mika Rautiainen ◽  
Jorma Laaksonen ◽  
...  
Keyword(s):  
Image Database ◽  
User Evaluation ◽  
Image Database Browsing

Selectivity of Inferior Temporal Neurons for Realistic Pictures Predicted by Algorithms for Image Database Navigation

2005 ◽  
Vol 94 (6) ◽  
pp. 4068-4081 ◽  
Author(s):  
Sarah Allred ◽  
Yan Liu ◽  
Bharathi Jagadeesh
Keyword(s):  
Human Perception ◽  
Image Database ◽  
Image Similarity ◽  
Perceptual Similarity ◽  
Neural Responses ◽  
Reference Images ◽  
Similar Images ◽  
Neural Selectivity ◽  
The Relationship ◽  
Realistic Images

Primates have a remarkable ability to perceive, recognize, and discriminate among the plethora of people, places, and things that they see, and neural selectivity in the primate inferotemporal (IT) cortex is thought to underlie this ability. Here we investigated the relationship between neural response and perception by recording from IT neurons in monkeys while they viewed realistic images. We then compared the similarity of neural responses elicited by images to the quantitative similarity of the images. Image similarity was approximated using several algorithms, two of which were designed to search image databases for perceptually similar images. Some algorithms for image similarity correlated well with human perception, and these algorithms explained part of the stimulus selectivity of IT neurons. Images that elicited similar neural responses were ranked as more similar by these algorithms than images that elicited different neural responses, and images ranked as similar by the algorithms elicited similar responses from neurons. Neural selectivity was predicted more accurately when the reference images for algorithm similarity elicited either very strong or very weak responses from the neuron. The degree to which algorithms for image similarity were correlated with human perception was related to the degree to which algorithms explained the selectivity of IT neurons, providing support for the proposal that the selectivity of IT neurons is related to perceptual similarity of images.

The Effect of Outcome Probability on Generalization in Predictive Learning

2019 ◽  
Vol 66 (1) ◽  
pp. 23-39 ◽  
Author(s):  
Hadar Ram ◽  
Dieter Struyf ◽  
Bram Vervliet ◽  
Gal Menahem ◽  
Nira Liberman
Keyword(s):  
High Probability ◽  
Lower Probability ◽  
Perceptual Similarity ◽  
Learning Paradigm ◽  
Predictive Learning ◽  
Lightning Bolt ◽  
Outcome Probability

Abstract. People apply what they learn from experience not only to the experienced stimuli, but also to novel stimuli. But what determines how widely people generalize what they have learned? Using a predictive learning paradigm, we examined the hypothesis that a low (vs. high) probability of an outcome following a predicting stimulus would widen generalization. In three experiments, participants learned which stimulus predicted an outcome (S+) and which stimulus did not (S−) and then indicated how much they expected the outcome after each of eight novel stimuli ranging in perceptual similarity to S+ and S−. The stimuli were rings of different sizes and the outcome was a picture of a lightning bolt. As hypothesized, a lower probability of the outcome widened generalization. That is, novel stimuli that were similar to S+ (but not to S−) produced expectations for the outcome that were as high as those associated with S+.

Sign in / Sign up

Export Citation Format

Share Document