The Experts below are selected from a list of 324 Experts worldwide ranked by ideXlab platform
Wei-zhan Hung - One of the best experts on this subject based on the ideXlab platform.
-
Applying linguistic information and Intersection concept to improve effectiveness of multi-criteria decision analysis technology
International Journal of Information Technology and Decision Making, 2014Co-Authors: Chen-tung Chen, Wei-zhan HungAbstract:Multi-criteria decision-making (MCDM) is one of the most widely used decision methodologies. Because every kind of MCDM approach has unique strengths and weaknesses, it is difficult to determine which kind of MCDM approach is best suited to a specific problem. Therefore, a new decision-making method is proposed herein, based on linguistic information and Intersection concepts; it is called the linguistic Intersection method (LIM). Notably, the linguistic variables are more suited to expressing the opinion of each decision maker. There are four MCDM methods: TOPSIS, ELECTRE, PROMETHEE and VIKOR which are included in the LIM. First, each MCDM approach is used to determine the ranking order of all alternatives in accordance with the linguistic evaluations of decision makers. Then, the Intersection Set is determined with regard to the better alternatives of all methods. Third, the final ranking order of alternatives in the Intersection Set can be determined by the proposed method. Lastly, an example is given to describe the procedure of the proposed method. In order to verify the effectiveness of the proposed method, a simulation test is provided to compare the LIM with the linguistic MCDM method. According to the comparison results, the proposed method is more stable in determining the ranking order of all decision alternatives.
-
Handling fuzzy decision making problem based on linguistic information and Intersection concept
2011 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE 2011), 2011Co-Authors: Chen-tung Chen, Wei-zhan HungAbstract:Multi-criteria decision-making (MCDM) is one of the most widely used decision methodologies. Because every kind of MCDM approach has its strong point and weakness, it is hard to make sure that what kind of MCDM approach is suitable to a specific problem. Therefore, a new decision making method is proposed in this paper based on linguistic information and Intersection concept which is called linguistic Intersection method (LIM). The linguistic variables are used to express the opinion of each decision-maker. There are four MCDM methods such as TOPSIS, ELECTRE, PROMETHEE, and VIKOR are included in the linguistic Intersection method. First, each MCDM approach is used to determine the ranking order of all alternatives in accordance with the linguistic evaluations by decision-makers. And then, the Intersection Set is determined for the better alternatives of all methods. Third, the final ranking order of alternatives in the Intersection Set can be determined by the proposed method. This study presented an example to implement and compare the proposed method with individual linguistic MCDM method. Finally, some conclusions and future research will be discussed at the end of this paper.
-
FUZZ-IEEE - Handling fuzzy decision making problem based on linguistic information and Intersection concept
2011 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE 2011), 2011Co-Authors: Chen-tung Chen, Wei-zhan HungAbstract:Multi-criteria decision-making (MCDM) is one of the most widely used decision methodologies. Because every kind of MCDM approach has its strong point and weakness, it is hard to make sure that what kind of MCDM approach is suitable to a specific problem. Therefore, a new decision making method is proposed in this paper based on linguistic information and Intersection concept which is called linguistic Intersection method (LIM). The linguistic variables are used to express the opinion of each decision-maker. There are four MCDM methods such as TOPSIS, ELECTRE, PROMETHEE, and VIKOR are included in the linguistic Intersection method. First, each MCDM approach is used to determine the ranking order of all alternatives in accordance with the linguistic evaluations by decision-makers. And then, the Intersection Set is determined for the better alternatives of all methods. Third, the final ranking order of alternatives in the Intersection Set can be determined by the proposed method. This study presented an example to implement and compare the proposed method with individual linguistic MCDM method. Finally, some conclusions and future research will be discussed at the end of this paper.
Chen-tung Chen - One of the best experts on this subject based on the ideXlab platform.
-
Applying linguistic information and Intersection concept to improve effectiveness of multi-criteria decision analysis technology
International Journal of Information Technology and Decision Making, 2014Co-Authors: Chen-tung Chen, Wei-zhan HungAbstract:Multi-criteria decision-making (MCDM) is one of the most widely used decision methodologies. Because every kind of MCDM approach has unique strengths and weaknesses, it is difficult to determine which kind of MCDM approach is best suited to a specific problem. Therefore, a new decision-making method is proposed herein, based on linguistic information and Intersection concepts; it is called the linguistic Intersection method (LIM). Notably, the linguistic variables are more suited to expressing the opinion of each decision maker. There are four MCDM methods: TOPSIS, ELECTRE, PROMETHEE and VIKOR which are included in the LIM. First, each MCDM approach is used to determine the ranking order of all alternatives in accordance with the linguistic evaluations of decision makers. Then, the Intersection Set is determined with regard to the better alternatives of all methods. Third, the final ranking order of alternatives in the Intersection Set can be determined by the proposed method. Lastly, an example is given to describe the procedure of the proposed method. In order to verify the effectiveness of the proposed method, a simulation test is provided to compare the LIM with the linguistic MCDM method. According to the comparison results, the proposed method is more stable in determining the ranking order of all decision alternatives.
-
Handling fuzzy decision making problem based on linguistic information and Intersection concept
2011 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE 2011), 2011Co-Authors: Chen-tung Chen, Wei-zhan HungAbstract:Multi-criteria decision-making (MCDM) is one of the most widely used decision methodologies. Because every kind of MCDM approach has its strong point and weakness, it is hard to make sure that what kind of MCDM approach is suitable to a specific problem. Therefore, a new decision making method is proposed in this paper based on linguistic information and Intersection concept which is called linguistic Intersection method (LIM). The linguistic variables are used to express the opinion of each decision-maker. There are four MCDM methods such as TOPSIS, ELECTRE, PROMETHEE, and VIKOR are included in the linguistic Intersection method. First, each MCDM approach is used to determine the ranking order of all alternatives in accordance with the linguistic evaluations by decision-makers. And then, the Intersection Set is determined for the better alternatives of all methods. Third, the final ranking order of alternatives in the Intersection Set can be determined by the proposed method. This study presented an example to implement and compare the proposed method with individual linguistic MCDM method. Finally, some conclusions and future research will be discussed at the end of this paper.
-
FUZZ-IEEE - Handling fuzzy decision making problem based on linguistic information and Intersection concept
2011 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE 2011), 2011Co-Authors: Chen-tung Chen, Wei-zhan HungAbstract:Multi-criteria decision-making (MCDM) is one of the most widely used decision methodologies. Because every kind of MCDM approach has its strong point and weakness, it is hard to make sure that what kind of MCDM approach is suitable to a specific problem. Therefore, a new decision making method is proposed in this paper based on linguistic information and Intersection concept which is called linguistic Intersection method (LIM). The linguistic variables are used to express the opinion of each decision-maker. There are four MCDM methods such as TOPSIS, ELECTRE, PROMETHEE, and VIKOR are included in the linguistic Intersection method. First, each MCDM approach is used to determine the ranking order of all alternatives in accordance with the linguistic evaluations by decision-makers. And then, the Intersection Set is determined for the better alternatives of all methods. Third, the final ranking order of alternatives in the Intersection Set can be determined by the proposed method. This study presented an example to implement and compare the proposed method with individual linguistic MCDM method. Finally, some conclusions and future research will be discussed at the end of this paper.
Qi Tian - One of the best experts on this subject based on the ideXlab platform.
-
Bayes Merging of Multiple Vocabularies for Scalable Image Retrieval
2014 IEEE Conference on Computer Vision and Pattern Recognition, 2014Co-Authors: Liang Zheng, Shengjin Wang, Wengang Zhou, Qi TianAbstract:In the Bag-of-Words (BoW) model, the vocabulary is of key importance. Typically, multiple vocabularies are generated to correct quantization artifacts and improve recall. However, this routine is corrupted by vocabulary correlation, i.e., overlapping among different vocabularies. Vocabulary correlation leads to an over-counting of the indexed features in the overlapped area, or the Intersection Set, thus compromising the retrieval accuracy. In order to address the correlation problem while preserve the benefit of high recall, this paper proposes a Bayes merging approach to down-weight the indexed features in the Intersection Set. Through explicitly modeling the correlation problem in a probabilistic view, a joint similarity on both image- and feature-level is estimated for the indexed features in the Intersection Set. We evaluate our method on three benchmark dataSets. Albeit simple, Bayes merging can be well applied in various merging tasks, and consistently improves the baselines on multi-vocabulary merging. Moreover, Bayes merging is efficient in terms of both time and memory cost, and yields competitive performance with the state-of-the-art methods.
-
CVPR - Bayes Merging of Multiple Vocabularies for Scalable Image Retrieval
2014 IEEE Conference on Computer Vision and Pattern Recognition, 2014Co-Authors: Liang Zheng, Shengjin Wang, Wengang Zhou, Qi TianAbstract:In the Bag-of-Words (BoW) model, the vocabulary is of key importance. Typically, multiple vocabularies are generated to correct quantization artifacts and improve recall. However, this routine is corrupted by vocabulary correlation, i.e., overlapping among different vocabularies. Vocabulary correlation leads to an over-counting of the indexed features in the overlapped area, or the Intersection Set, thus compromising the retrieval accuracy. In order to address the correlation problem while preserve the benefit of high recall, this paper proposes a Bayes merging approach to down-weight the indexed features in the Intersection Set. Through explicitly modeling the correlation problem in a probabilistic view, a joint similarity on both image- and feature-level is estimated for the indexed features in the Intersection Set. We evaluate our method on three benchmark dataSets. Albeit simple, Bayes merging can be well applied in various merging tasks, and consistently improves the baselines on multi-vocabulary merging. Moreover, Bayes merging is efficient in terms of both time and memory cost, and yields competitive performance with the state-of-the-art methods.
-
Bayes Merging of Multiple Vocabularies for Scalable Image Retrieval
arXiv: Computer Vision and Pattern Recognition, 2014Co-Authors: Liang Zheng, Shengjin Wang, Wengang Zhou, Qi TianAbstract:The Bag-of-Words (BoW) representation is well applied to recent state-of-the-art image retrieval works. Typically, multiple vocabularies are generated to correct quantization artifacts and improve recall. However, this routine is corrupted by vocabulary correlation, i.e., overlapping among different vocabularies. Vocabulary correlation leads to an over-counting of the indexed features in the overlapped area, or the Intersection Set, thus compromising the retrieval accuracy. In order to address the correlation problem while preserve the benefit of high recall, this paper proposes a Bayes merging approach to down-weight the indexed features in the Intersection Set. Through explicitly modeling the correlation problem in a probabilistic view, a joint similarity on both image- and feature-level is estimated for the indexed features in the Intersection Set. We evaluate our method through extensive experiments on three benchmark dataSets. Albeit simple, Bayes merging can be well applied in various merging tasks, and consistently improves the baselines on multi-vocabulary merging. Moreover, Bayes merging is efficient in terms of both time and memory cost, and yields competitive performance compared with the state-of-the-art methods.
Liang Zheng - One of the best experts on this subject based on the ideXlab platform.
-
Bayes Merging of Multiple Vocabularies for Scalable Image Retrieval
2014 IEEE Conference on Computer Vision and Pattern Recognition, 2014Co-Authors: Liang Zheng, Shengjin Wang, Wengang Zhou, Qi TianAbstract:In the Bag-of-Words (BoW) model, the vocabulary is of key importance. Typically, multiple vocabularies are generated to correct quantization artifacts and improve recall. However, this routine is corrupted by vocabulary correlation, i.e., overlapping among different vocabularies. Vocabulary correlation leads to an over-counting of the indexed features in the overlapped area, or the Intersection Set, thus compromising the retrieval accuracy. In order to address the correlation problem while preserve the benefit of high recall, this paper proposes a Bayes merging approach to down-weight the indexed features in the Intersection Set. Through explicitly modeling the correlation problem in a probabilistic view, a joint similarity on both image- and feature-level is estimated for the indexed features in the Intersection Set. We evaluate our method on three benchmark dataSets. Albeit simple, Bayes merging can be well applied in various merging tasks, and consistently improves the baselines on multi-vocabulary merging. Moreover, Bayes merging is efficient in terms of both time and memory cost, and yields competitive performance with the state-of-the-art methods.
-
CVPR - Bayes Merging of Multiple Vocabularies for Scalable Image Retrieval
2014 IEEE Conference on Computer Vision and Pattern Recognition, 2014Co-Authors: Liang Zheng, Shengjin Wang, Wengang Zhou, Qi TianAbstract:In the Bag-of-Words (BoW) model, the vocabulary is of key importance. Typically, multiple vocabularies are generated to correct quantization artifacts and improve recall. However, this routine is corrupted by vocabulary correlation, i.e., overlapping among different vocabularies. Vocabulary correlation leads to an over-counting of the indexed features in the overlapped area, or the Intersection Set, thus compromising the retrieval accuracy. In order to address the correlation problem while preserve the benefit of high recall, this paper proposes a Bayes merging approach to down-weight the indexed features in the Intersection Set. Through explicitly modeling the correlation problem in a probabilistic view, a joint similarity on both image- and feature-level is estimated for the indexed features in the Intersection Set. We evaluate our method on three benchmark dataSets. Albeit simple, Bayes merging can be well applied in various merging tasks, and consistently improves the baselines on multi-vocabulary merging. Moreover, Bayes merging is efficient in terms of both time and memory cost, and yields competitive performance with the state-of-the-art methods.
-
Bayes Merging of Multiple Vocabularies for Scalable Image Retrieval
arXiv: Computer Vision and Pattern Recognition, 2014Co-Authors: Liang Zheng, Shengjin Wang, Wengang Zhou, Qi TianAbstract:The Bag-of-Words (BoW) representation is well applied to recent state-of-the-art image retrieval works. Typically, multiple vocabularies are generated to correct quantization artifacts and improve recall. However, this routine is corrupted by vocabulary correlation, i.e., overlapping among different vocabularies. Vocabulary correlation leads to an over-counting of the indexed features in the overlapped area, or the Intersection Set, thus compromising the retrieval accuracy. In order to address the correlation problem while preserve the benefit of high recall, this paper proposes a Bayes merging approach to down-weight the indexed features in the Intersection Set. Through explicitly modeling the correlation problem in a probabilistic view, a joint similarity on both image- and feature-level is estimated for the indexed features in the Intersection Set. We evaluate our method through extensive experiments on three benchmark dataSets. Albeit simple, Bayes merging can be well applied in various merging tasks, and consistently improves the baselines on multi-vocabulary merging. Moreover, Bayes merging is efficient in terms of both time and memory cost, and yields competitive performance compared with the state-of-the-art methods.
T. Pavlidis - One of the best experts on this subject based on the ideXlab platform.
-
Feature analysis using line sweep thinning algorithm
IEEE Transactions on Pattern Analysis and Machine Intelligence, 1999Co-Authors: Fu Chang, Ya-ching Lu, T. PavlidisAbstract:We propose a new thinning algorithm based on line sweep operation. Assuming that the contour of the figure to be thinned has been approximated by polygons, the "events" are then the vertices of the polygons, and the line sweep algorithm searches for pairs of edges lying within each slab. The pairing of edges is useful for detecting both regular and Intersection regions. The regular regions can be found at the sites where pairings between edges exist. Intersection regions are those where such relations would cease to exist. A salient feature of our approach is that it finds simultaneously the Set of regular regions that attach to the same Intersection region. Such a Set is thus called an Intersection Set. The output of our algorithm consists of skeletons as well as Intersection Sets, both can be used as features for subsequent character recognition. Moreover, the line sweep thinning algorithm is efficient in computation as compared with a pixel-based thinning algorithm which outputs skeletons only.
-
ICDAR - Line sweep thinning algorithm for feature analysis
Proceedings of the Fourth International Conference on Document Analysis and Recognition, 1997Co-Authors: Fu Chang, Ya-ching Lu, T. PavlidisAbstract:In a previous article (Proc. 3rd Int. Conf. Document Anal. and Recogn., Montreal, Canada, pp. 227-30, 1995), we showed that a line sweep algorithm is an efficient means of line thinning. A line sweep is a process that works on polygonal figures and pairs the edges that bound the figure interior from two sides. In this article, we improve and extend this approach in the following way. First, a new method is used for grouping paired edges into regular and Intersection regions. The regular regions can be found at the site where pairings between edges exist. Intersection regions, on the other hand, are where such relations cease to exist, due to the fact that pair relations between edges of wide distance are cancelled. Secondly, a salient feature of our new approach is to simultaneously find the Set of regular regions that attach to the same Intersection region. Such a Set is called an Intersection Set. The output of our algorithm consists of skeletons as well as Intersection Sets. Both of them can be used as features for subsequent character recognition. Moreover, the line sweep thinning algorithm is efficient in computation as compared with a pixel-based thinning algorithm which outputs skeletons only.
-
Line sweep thinning algorithm for feature analysis
Proceedings of the Fourth International Conference on Document Analysis and Recognition, 1997Co-Authors: Fu Chang, Ya-ching Lu, T. PavlidisAbstract:In a previous article (Proc. 3rd Int. Conf. Document Anal. and Recogn., Montreal, Canada, pp. 227-30, 1995), we showed that a line sweep algorithm is an efficient means of line thinning. A line sweep is a process that works on polygonal figures and pairs the edges that bound the figure interior from two sides. In this article, we improve and extend this approach in the following way. First, a new method is used for grouping paired edges into regular and Intersection regions. The regular regions can be found at the site where pairings between edges exist. Intersection regions, on the other hand, are where such relations cease to exist, due to the fact that pair relations between edges of wide distance are cancelled. Secondly, a salient feature of our new approach is to simultaneously find the Set of regular regions that attach to the same Intersection region. Such a Set is called an Intersection Set. The output of our algorithm consists of skeletons as well as Intersection Sets. Both of them can be used as features for subsequent character recognition. Moreover, the line sweep thinning algorithm is efficient in computation as compared with a pixel-based thinning algorithm which outputs skeletons only.
