Fast Fractal Encoding through FFT using Modified Crosscorrelation based Similarity Measure

2010 ◽  
Author(s):  
S.B Dhok ◽  
R.B. Deshmukh ◽  
A.G. Keskar
Keyword(s):  
Similarity Measure ◽  
Fractal Encoding

scholarly journals Fractal Video Coding Using Fast Normalized Covariance Based Similarity Measure

2016 ◽  
Vol 2016 ◽  
pp. 1-11 ◽  
Author(s):  
Ravindra E. Chaudhari ◽  
Sanjay B. Dhok
Keyword(s):  
Video Compression ◽  
Similarity Measure ◽  
Compression Ratio ◽  
Prediction Error ◽  
Experimental Result ◽  
Gray Level ◽  
Search Window ◽  
Target Level ◽  
Compression Time ◽  
Fractal Encoding

Fast normalized covariance based similarity measure for fractal video compression with quadtree partitioning is proposed in this paper. To increase the speed of fractal encoding, a simplified expression of covariance between range and overlapped domain blocks within a search window is implemented in frequency domain. All the covariance coefficients are normalized by using standard deviation of overlapped domain blocks and these are efficiently calculated in one computation by using two different approaches, namely, FFT based and sum table based. Results of these two approaches are compared and they are almost equal to each other in all aspects, except the memory requirement. Based on proposed simplified similarity measure, gray level transformation parameters are computationally modified and isometry transformations are performed using rotation/reflection properties of IFFT. Quadtree decompositions are used for the partitions of larger size of range block, that is, 16 × 16, which is based on target level of motion compensated prediction error. Experimental result shows that proposed method can increase the encoding speed and compression ratio by 66.49% and 9.58%, respectively, as compared to NHEXS method with increase in PSNR by 0.41 dB. Compared to H.264, proposed method can save 20% of compression time with marginal variation in PSNR and compression ratio.

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

A Semantic Similarity Measure between Ontological Concepts

2012 ◽  
Vol 38 (2) ◽  
pp. 229-235 ◽  
Author(s):  
Wen-Qing LI ◽  
Xin SUN ◽  
Chang-You ZHANG ◽  
Ye FENG
Keyword(s):  
Semantic Similarity ◽  
Similarity Measure ◽  
Semantic Similarity Measure

SIMILARITY MEASURE USING SIGN DISTANCE

2020 ◽  
Vol 10 (1) ◽  
pp. 193-197
Author(s):  
D. Stephen Dinagar ◽  
E. Fany Helena
Keyword(s):  
Similarity Measure

MATHURA (MBI) - A NOVEL IMPUTATION MEASURE FOR IMPUTATION OF MISSING VALUES IN MEDICAL DATASETS

2019 ◽  
Vol 13 ◽  
Author(s):  
B. Mathura Bai ◽  
N. Mangathayaru ◽  
B. Padmaja Rani ◽  
Shadi Aljawarneh
Keyword(s):  
Similarity Measure ◽  
Medical Records ◽  
Missing Values ◽  
Similarity Measures ◽  
Common Problems ◽  
Experiment Analysis

: Missing attribute values in medical datasets are one of the most common problems faced when mining medical datasets. Estimation of missing values is a major challenging task in pre-processing of datasets. Any wrong estimate of missing attribute values can lead to inefficient and improper classification thus resulting in lower classifier accuracies. Similarity measures play a key role during the imputation process. The use of an appropriate and better similarity measure can help to achieve better imputation and improved classification accuracies. This paper proposes a novel imputation measure for finding similarity between missing and non-missing instances in medical datasets. Experiments are carried by applying both the proposed imputation technique and popular benchmark existing imputation techniques. Classification is carried using KNN, J48, SMO and RBFN classifiers. Experiment analysis proved that after imputation of medical records using proposed imputation technique, the resulting classification accuracies reported by the classifiers KNN, J48 and SMO have improved when compared to other existing benchmark imputation techniques.

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