A perceptual similarity measure based on smoothing filters and the normalized compression distance

Author(s):  
Nicholas Tran
Leonardo ◽  
2020 ◽  
Vol 53 (3) ◽  
pp. 274-280
Author(s):  
Alan Marsden

Information Theory provoked the interest of arts researchers from its inception in the mid-twentieth century but failed to produce the expected impact, partly because the data and computing systems required were not available. With the modern availability of data from public collections and sophisticated software, there is renewed interest in Information Theory. Successful application in the analysis of music implies potential success in other art forms also. The author gives an illustrative example, applying the Information-Theoretic similarity measure normalized compression distance with the aim of ranking paintings in a large collection by their conventionality.


2017 ◽  
Vol 47 (11) ◽  
pp. 2956-2966 ◽  
Author(s):  
Yuming Fang ◽  
Zhijun Fang ◽  
Feiniu Yuan ◽  
Yong Yang ◽  
Shouyuan Yang ◽  
...  

Author(s):  
Hadar Ram ◽  
Dieter Struyf ◽  
Bram Vervliet ◽  
Gal Menahem ◽  
Nira Liberman

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+.


Author(s):  
Mohana Priya K ◽  
Pooja Ragavi S ◽  
Krishna Priya G

Clustering is the process of grouping objects into subsets that have meaning in the context of a particular problem. It does not rely on predefined classes. It is referred to as an unsupervised learning method because no information is provided about the "right answer" for any of the objects. Many clustering algorithms have been proposed and are used based on different applications. Sentence clustering is one of best clustering technique. Hierarchical Clustering Algorithm is applied for multiple levels for accuracy. For tagging purpose POS tagger, porter stemmer is used. WordNet dictionary is utilized for determining the similarity by invoking the Jiang Conrath and Cosine similarity measure. Grouping is performed with respect to the highest similarity measure value with a mean threshold. This paper incorporates many parameters for finding similarity between words. In order to identify the disambiguated words, the sense identification is performed for the adjectives and comparison is performed. semcor and machine learning datasets are employed. On comparing with previous results for WSD, our work has improvised a lot which gives a percentage of 91.2%


Informatica ◽  
2018 ◽  
Vol 29 (3) ◽  
pp. 399-420
Author(s):  
Alessia Amelio ◽  
Darko Brodić ◽  
Radmila Janković

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