scholarly journals Fast Search Method Based on Vector Quantization for Raman Spectroscopy Identification

Mathematics ◽  
2020 ◽  
Vol 8 (11) ◽  
pp. 1970
Author(s):  
Jun-Kyu Park ◽  
Suwoong Lee ◽  
Aaron Park ◽  
Sung-June Baek
Keyword(s):  
Search Algorithm ◽  
Principal Component ◽  
Search Method ◽  
Reference Spectrum ◽  
Search Range ◽  
Fast Search ◽  
Input Spectrum ◽  
Computationally Intensive ◽  
Multiple Clusters ◽  
Full Search

In spectroscopy, matching a measured spectrum to a reference spectrum in a large database is often computationally intensive. To solve this problem, we propose a novel fast search algorithm that finds the most similar spectrum in the database. The proposed method is based on principal component transformation and provides results equivalent to the traditional full search method. To reduce the search range, hierarchical clustering is employed, which divides the spectral data into multiple clusters according to the similarity of the spectrum, allowing the search to start at the cluster closest to the input spectrum. Furthermore, a pilot search was applied in advance to further accelerate the search. Experimental results show that the proposed method requires only a small fraction of the computational complexity required by the full search, and it outperforms the previous methods.

A MULTI-SCALE TWO-STEP FAST SEARCH ALGORITHM FOR BLOCK MOTION ESTIMATION

2002 ◽  
Vol 02 (04) ◽  
pp. 633-653
Author(s):  
SHIH-HAO KE ◽  
TSU-TIAN LEE
Keyword(s):  
Motion Estimation ◽  
Mean Squared Error ◽  
Search Algorithm ◽  
Large Displacement ◽  
Fast Search ◽  
Multi Scale ◽  
Block Motion ◽  
Computationally Intensive ◽  
Block Motion Estimation ◽  
Full Search

Block motion estimation using full search is computationally intensive. Previously proposed fast algorithms reduce computation by limiting the number of search locations and search directions in a predefined search region. This is accomplished at the expense of accuracy of motion estimation and a large mean squared error for motion-compensated images, especially for image sequences with large displacement and rotation. In this paper, a novel efficient search algorithm for block motion estimation is presented to produce better performance than some fast search algorithms that have been developed, such as three step search, orthogonal search, 2D-logarithmic search, four step search, and block-based gradient descent search, in large displacement and rotation image cases. The proposed algorithm is based on the notion of locally multi-scale operation, search of global minimum, and two layer search strategy. Experimental results show that the proposed algorithm produces anticipative performance while costing much less computation power than the full search algorithm.

A Fast Image Encoding Algorithm Based on the Pyramid Structure of Codewords

2011 ◽  
pp. 214-226
Author(s):  
Ahmed A. Radwan ◽  
Ahmed Swilem ◽  
Mamdouh M. Gomaa
Keyword(s):  
Vector Quantization ◽  
Search Algorithm ◽  
Search Algorithms ◽  
Search Procedure ◽  
Image Encoding ◽  
Fast Search ◽  
Pyramid Structure ◽  
Speed Up ◽  
Simulation Results ◽  
Full Search

This article presents a very simple and efficient algorithm for codeword search in the vector quantization encoding. This algorithm uses 2-pixel merging norm pyramid structure to speed up the closest codeword search process. The authors first derive a condition to eliminate unnecessary matching operations from the search procedure. Then, based on this elimination condition, a fast search algorithm is suggested. Simulation results show that, the proposed search algorithm reduces the encoding complexity while maintaining the same encoding quality as that of the full search algorithm. It is also found that the proposed algorithm outperforms the existing search algorithms.

scholarly journals Direction-Oriented Fast Full Search Algorithm at the Divided Search Range

2007 ◽  
Vol 12 (3) ◽  
pp. 278-288
Author(s):  
Dong-Young Lim ◽  
Sang-Jun Park ◽  
Je-Chang Jeong
Keyword(s):  
Search Algorithm ◽  
Search Range ◽  
Full Search

scholarly journals AN EFFICIENT MOTION ESTIMATION ALGORITHM BASED ON PARTICLE SWARM OPTIMIZATION

2014 ◽  
pp. 199-203
Author(s):  
ASHA ELIZABETH JACOB ◽  
IMMANUEL ALEX PANDIAN
Keyword(s):  
Motion Estimation ◽  
Search Algorithm ◽  
Pso Algorithm ◽  
Search Method ◽  
Block Matching ◽  
Estimation Accuracy ◽  
Diamond Search ◽  
Diamond Search Algorithm ◽  
Block Based ◽  
Full Search

The PSO algorithm reduce the search points without the degradation of the image quality. It provides accurate motion estimation with very low complexity in the context of video estimation. This algorithm is capable of reducing the computational complexity of block matching process. This algorithm maintains high estimation accuracy compared to the full search method. The critical component in most block-based video compression system is Motion Estimation because redundancy between successive frames of video sequence allows for compression of video data. These algorithms are used to reduce the computational requirement by checking only some points inside the search window, while keeping a good error performance when compared with Full Search and Diamond search algorithm. This algorithm should maintain high estimation accuracy compared to the Full search method and Diamond search algorithm. Here by using the PSO algorithm could get a high accuracy in the block-based motion estimation.

Fuzzy Sets and Inverted Search - Two Concepts for Compound Identification in Spectra

1983 ◽  
Vol 27 ◽  
pp. 27-34
Author(s):  
Thomas Blaffert
Keyword(s):  
Fuzzy Sets ◽  
Powder Diffraction ◽  
Search Algorithm ◽  
Search Method ◽  
Compound Identification ◽  
Current Application ◽  
New Approach ◽  
X Ray ◽  
Fast Search ◽  
Fast Search Algorithm

AbstractThis paper describes a new approach to the interpretation of spectra with ‘fuzzy sets’ and a fast search algorithm for spectrum libraries, using a combination of this theory and the inverse search method. The concepts are applied in a computer program named CIF (Compound identification with Inverted search and Fuzzy sets) which is used in the current application for the interpretation of X-ray powder diffraction signals. The methods are readily applicable to other kinds of spectra such as IR, UV, chromatrography etc.

Automatic Mining of Quantitative Association Rules with Gravitational Search Algorithm

2017 ◽  
Vol 27 (03) ◽  
pp. 343-372 ◽  
Author(s):  
Umit Can ◽  
Bilal Alatas
Keyword(s):  
Association Rules ◽  
Optimization Problems ◽  
Search Algorithm ◽  
Fitness Function ◽  
Optimization Algorithms ◽  
Gravitational Search Algorithm ◽  
Search Space ◽  
Search Method ◽  
Quantitative Association Rules ◽  
Gravitational Search

The classical optimization algorithms are not efficient in solving complex search and optimization problems. Thus, some heuristic optimization algorithms have been proposed. In this paper, exploration of association rules within numerical databases with Gravitational Search Algorithm (GSA) has been firstly performed. GSA has been designed as search method for quantitative association rules from the databases which can be regarded as search space. Furthermore, determining the minimum values of confidence and support for every database which is a hard job has been eliminated by GSA. Apart from this, the fitness function used for GSA is very flexible. According to the interested problem, some parameters can be removed from or added to the fitness function. The range values of the attributes have been automatically adjusted during the time of mining of the rules. That is why there is not any requirements for the pre-processing of the data. Attributes interaction problem has also been eliminated with the designed GSA. GSA has been tested with four real databases and promising results have been obtained. GSA seems an effective search method for complex numerical sequential patterns mining, numerical classification rules mining, and clustering rules mining tasks of data mining.

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