As the popularity of digital images is rapidly increasing on the Internet, research on technologies for semantic image classification has become an important research topic. However, the well-known content-based image...As the popularity of digital images is rapidly increasing on the Internet, research on technologies for semantic image classification has become an important research topic. However, the well-known content-based image classification methods do not overcome the so-called semantic gap problem in which low-level visual features cannot represent the high-level semantic content of images. Image classification using visual and textual information often performs poorly since the extracted textual features are often too limited to accurately represent the images. In this paper, we propose a semantic image classification ap- proach using multi-context analysis. For a given image, we model the relevant textual information as its multi-modal context, and regard the related images connected by hyperlinks as its link context. Two kinds of context analysis models, i.e., cross-modal correlation analysis and link-based correlation model, are used to capture the correlation among different modals of features and the topical dependency among images induced by the link structure. We propose a new collective classification model called relational support vector classifier (RSVC) based on the well-known Support Vector Machines (SVMs) and the link-based cor- relation model. Experiments showed that the proposed approach significantly improved classification accuracy over that of SVM classifiers using visual and/or textual features.展开更多
Current research on metaphor analysis is generally knowledge-based and corpus-based,which calls for methods of automatic feature extraction and weight calculation.Combining natural language processing(NLP),latent sema...Current research on metaphor analysis is generally knowledge-based and corpus-based,which calls for methods of automatic feature extraction and weight calculation.Combining natural language processing(NLP),latent semantic analysis(LSA),and Pearson correlation coefficient,this paper proposes a metaphor analysis method for extracting the content words from both literal and metaphorical corpus,calculating correlation degree,and analyzing their relationships.The value of the proposed method was demonstrated through a case study by using a corpus with keyword“飞翔(fly)”.When compared with the method of Pearson correlation coefficient,the experiment shows that the LSA can produce better results with greater significance in correlation degree.It is also found that the number of common words that appeared in both literal and metaphorical word bags decreased with the correlation degree.The case study also revealed that there are more nouns appear in literal corpus,and more adjectives and adverbs appear in metaphorical corpus.The method proposed will benefit NLP researchers to develop the required step-by-step calculation tools for accurate quantitative analysis.展开更多
Electrocardiogram(ECG)biometric recognition has gained considerable attention,and various methods have been proposed to facilitate its development.However,one limitation is that the diversity of ECG signals affects th...Electrocardiogram(ECG)biometric recognition has gained considerable attention,and various methods have been proposed to facilitate its development.However,one limitation is that the diversity of ECG signals affects the recognition performance.To address this issue,in this paper,we propose a novel ECG biometrics framework based on enhanced correlation and semantic-rich embedding.Firstly,we construct an enhanced correlation between the base feature and latent representation by using only one projection.Secondly,to fully exploit the semantic information,we take both the label and pairwise similarity into consideration to reduce the influence of ECG sample diversity.Furthermore,to solve the objective function,we propose an effective and efficient algorithm for optimization.Finally,extensive experiments are conducted on two benchmark datasets,and the experimental results show the effectiveness of our framework.展开更多
基金Project supported by the Hi-Tech Research and Development Pro-gram (863) of China (No. 2003AA119010), and China-American Digital Academic Library (CADAL) Project (No. CADAL2004002)
文摘As the popularity of digital images is rapidly increasing on the Internet, research on technologies for semantic image classification has become an important research topic. However, the well-known content-based image classification methods do not overcome the so-called semantic gap problem in which low-level visual features cannot represent the high-level semantic content of images. Image classification using visual and textual information often performs poorly since the extracted textual features are often too limited to accurately represent the images. In this paper, we propose a semantic image classification ap- proach using multi-context analysis. For a given image, we model the relevant textual information as its multi-modal context, and regard the related images connected by hyperlinks as its link context. Two kinds of context analysis models, i.e., cross-modal correlation analysis and link-based correlation model, are used to capture the correlation among different modals of features and the topical dependency among images induced by the link structure. We propose a new collective classification model called relational support vector classifier (RSVC) based on the well-known Support Vector Machines (SVMs) and the link-based cor- relation model. Experiments showed that the proposed approach significantly improved classification accuracy over that of SVM classifiers using visual and/or textual features.
基金Fundamental Research Funds for the Central Universities of Ministry of Education of China(No.19D111201)。
文摘Current research on metaphor analysis is generally knowledge-based and corpus-based,which calls for methods of automatic feature extraction and weight calculation.Combining natural language processing(NLP),latent semantic analysis(LSA),and Pearson correlation coefficient,this paper proposes a metaphor analysis method for extracting the content words from both literal and metaphorical corpus,calculating correlation degree,and analyzing their relationships.The value of the proposed method was demonstrated through a case study by using a corpus with keyword“飞翔(fly)”.When compared with the method of Pearson correlation coefficient,the experiment shows that the LSA can produce better results with greater significance in correlation degree.It is also found that the number of common words that appeared in both literal and metaphorical word bags decreased with the correlation degree.The case study also revealed that there are more nouns appear in literal corpus,and more adjectives and adverbs appear in metaphorical corpus.The method proposed will benefit NLP researchers to develop the required step-by-step calculation tools for accurate quantitative analysis.
基金supported by National Natural Science Foundation of China(No.62076151)Natural Science Foundation of Shandong Province,China(No.ZR2020 MF052)the NSFC-Xinjiang Joint Fund,China(No.U1903127)。
文摘Electrocardiogram(ECG)biometric recognition has gained considerable attention,and various methods have been proposed to facilitate its development.However,one limitation is that the diversity of ECG signals affects the recognition performance.To address this issue,in this paper,we propose a novel ECG biometrics framework based on enhanced correlation and semantic-rich embedding.Firstly,we construct an enhanced correlation between the base feature and latent representation by using only one projection.Secondly,to fully exploit the semantic information,we take both the label and pairwise similarity into consideration to reduce the influence of ECG sample diversity.Furthermore,to solve the objective function,we propose an effective and efficient algorithm for optimization.Finally,extensive experiments are conducted on two benchmark datasets,and the experimental results show the effectiveness of our framework.
