• International Journal of Fuzzy Logic and Intelligent Systems
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• v.6 no.2
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• pp.100-104
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• 2006

Fuzzy techniques can be applied in many domains of computer vision community. The definition of an adequate similarity measure for measuring the similarity between fuzzy sets is of great importance in the field of image processing, image retrieval and pattern recognition. This paper proposes a new class of the similarity measures. The properties, sensitivity and effectiveness of the proposed measures are investigated and tested on real data. Experimental results show that these similarity measures can provide a useful way for measuring the similarity between fuzzy sets.

• Phonetics and Speech Sciences
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• v.1 no.3
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• pp.109-115
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• 2009

Many isolated word recognition systems may generate non-similar words for recognition candidates because they use only acoustic information. In this paper, we investigate several techniques which can exclude non-similar words from N-best candidate words by applying Levenstein distance measure. At first, word distance method based on phone and syllable distances are considered. These methods use just Levenstein distance on phones or double Levenstein distance algorithm on syllables of candidates. Next, word similarity approaches are presented that they use characters' position information of word candidates. Each character's position is labeled to inserted, deleted, and correct position after alignment between source and target string. The word similarities are obtained from characters' positional probabilities which mean the frequency ratio of the same characters' observations on the position. From experimental results, we can find that the proposed methods are effective for removing non-similar words without loss of system performance from the N-best recognition candidates of the systems.

• The Transactions of the Korea Information Processing Society
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• v.7 no.3
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• pp.1025-1031
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• 2000

Pre-existing pattern recognition techniques, in the case of character recognition, have limited on the application field. But CAI character learning system and writer's recognition system are very important parts. The application field of pre-existing system can be expanded in the content that the learning of characters and the recognition of writers in the proposed paper. In order to achieve these goals, the development contents are the following: Firstly, pre-processing method by understanding the image structure is proposed, secondly, recognition of characters are accomplished b the histogram distribution characteristics. Finally, similarity measure functions are defined from standard character pattern for matching of the input character pattern. Also the effectiveness of this system is demonstrated by experimenting the standard primitive image.

• Journal of Computational Design and Engineering
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• v.3 no.3
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• pp.274-285
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• 2016

A designer is mainly supported by two essential factors in design decisions. These two factors are intelligence and experience aiding the designer by predicting the interconnection between the required design parameters. Through classification of product data and similarity recognition between new and existing designs, it is partially possible to replace the required experience for an inexperienced designer. Given this context, the current paper addresses a framework for recognition and flexible retrieval of similar models in product design. The idea is to establish an infrastructure for transferring design as well as the required PLM (Product Lifecycle Management) know-how to the design phase of product development in order to reduce the design time. Furthermore, such a method can be applied as a brainstorming method for a new and creative product development as well. The proposed framework has been tested and benchmarked while showing promising results.

• Journal of the Korea Society of Computer and Information
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• v.21 no.11
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• pp.85-91
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• 2016

This paper proposes a collective approach to cited text recognition by exploiting a set of citing text from different articles citing the same article. First, the proposed method gathers highly-ranked cited sentences from the cited article using a group of citing text to create a collective information of probable cited sentences. Then, such collective information is used to determine final cited sentences among highly-ranked sentences from similarity-based cited text recognition. Experiments have been conducted on the data set which consists of research articles from a computational linguistics domain. Evaluation results showed that the proposed method could improve the performance of similarity-based baseline approaches.

• Journal of Korea Multimedia Society
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• v.23 no.12
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• pp.1552-1564
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• 2020

As various styles of fonts were used, there were problems such as output errors due to uninstalled fonts and difficulty in font recognition. To solve these problems, research on font recognition and recommendation were actively conducted. However, Hangul font research remains at the basic level. Therefore, in order to automate the comparison on Hangul font similarity in the future, we analyze the influence of each stroke element property. First, we select seven representative properties based on Hangul stroke shape elements. Second, we design a calculation model to compare similarity between fonts. Third, we analyze the effect of each stroke element through the cosine similarity between the user's evaluation and the results of the model. As a result, there was no significant difference in the individual effect of each representative property. Also, the more accurate similarity comparison was possible when many representative properties were used.

• Journal of the Korean Institute of Intelligent Systems
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• v.20 no.3
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• pp.343-347
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• 2010

This paper presents an efficient image recognition method using the hybrid coefficient measure of correlation and distance. The correlation coefficient is applied to measure the statistical similarity by using Pearson coefficient, and distance coefficient is also applied to measure the spacial similarity by using city-block. The total similarity among images is calculated by extending the similarity between the feature vectors, then the feature vectors can be extracted by PCA and ICA, respectively. The proposed method has been applied to the problem for recognizing the 960(30 persons * 4 expressions * 2 lights * 4 poses) facial images of 40*50 pixels. The experimental results show that the proposed method of ICA has a superior recognition performances than the method using PCA, and is affected less by the environmental influences so as lighting.

• International Journal of Advanced Culture Technology
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• v.7 no.1
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• pp.64-69
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• 2019

With the advent of the fourth industry, the need for office automation with automatic character recognition capabilities is increasing day by day. Therefore, in this paper, we study a character recognition algorithm that effectively recognizes a new experimental data character by using learning data characters. The proposed algorithm computes the degree of similarity that the structural regions of learning data characters match the corresponding regions of the experimental data character. It has been confirmed that satisfactory results can be obtained by selecting the learning data character with the highest degree of similarity in the matching process as the final recognition result for a given experimental data character.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.9 no.11
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• pp.4502-4518
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• 2015

Fisher linear discriminant analysis (LDA) is one of the most popular projection techniques for feature extraction and has been widely applied in face recognition. However, it cannot be used when encountering the single sample per person problem (SSPP) because the intra-class variations cannot be evaluated. In this paper, we propose a novel method called local similarity based linear discriminant analysis (LS_LDA) to solve this problem. Motivated by the "divide-conquer" strategy, we first divide the face into local blocks, and classify each local block, and then integrate all the classification results to make final decision. To make LDA feasible for SSPP problem, we further divide each block into overlapped patches and assume that these patches are from the same class. To improve the robustness of LS_LDA to outliers, we further propose local similarity based median discriminant analysis (LS_MDA), which uses class median vector to estimate the class population mean in LDA modeling. Experimental results on three popular databases show that our methods not only generalize well SSPP problem but also have strong robustness to expression, illumination, occlusion and time variation.

• Journal of Information Processing Systems
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• v.14 no.3
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• pp.790-800
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• 2018

The simplified neutrosophic set (SNS) is a generalization of fuzzy set that is designed for some practical situations in which each element has truth membership function, indeterminacy membership function and falsity membership function. In this paper, we propose a new method to construct similarity measures of single valued neutrosophic sets (SVNSs) and interval valued neutrosophic sets (IVNSs), respectively. Then we prove that the proposed formulas satisfy the axiomatic definition of the similarity measure. At last, we apply them to pattern recognition under the single valued neutrosophic environment and multi-criteria decision-making problems under the interval valued neutrosophic environment. The results show that our methods are effective and reasonable.