scholarly journals Relocated Colour Contrast Occurrence Matrix and Adapted Similarity Measure for Colour Texture Retrieval

2018 ◽  
pp. 609-619 ◽  
Author(s):  
Hela Jebali ◽  
Noel Richard ◽  
Hermine Chatoux ◽  
Mohamed Naouai
Keyword(s):  
Similarity Measure ◽  
Colour Contrast ◽  
Occurrence Matrix ◽  
Texture Retrieval

An Ordinal Co-occurrence Matrix Framework for Texture Retrieval

2007 ◽  
Vol 2007 (1) ◽  
pp. 017358 ◽  
Author(s):  
Mari Partio ◽  
Bogdan Cramariuc ◽  
Moncef Gabbouj
Keyword(s):  
Occurrence Matrix ◽  
Texture Retrieval

scholarly journals An Ordinal Co-occurrence Matrix Framework for Texture Retrieval

2007 ◽  
Vol 2007 ◽  
pp. 1-15 ◽  
Author(s):  
Mari Partio ◽  
Bogdan Cramariuc ◽  
Moncef Gabbouj
Keyword(s):  
Occurrence Matrix ◽  
Texture Retrieval

Handling WSD using Hierarchical Clustering Algorithm with sentences

2018 ◽  
pp. 83-88
Author(s):  
Mohana Priya K ◽  
Pooja Ragavi S ◽  
Krishna Priya G
Keyword(s):  
Hierarchical Clustering ◽  
Similarity Measure ◽  
Clustering Algorithm ◽  
Clustering Algorithms ◽  
Cosine Similarity Measure ◽  
Hierarchical Clustering Algorithm ◽  
Multiple Levels ◽  
Pos Tagger ◽  
Sentence Clustering ◽  
The Right

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%

The Extended-Average Common Submatrix Similarity Measure with Application to Handwritten Character Images

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