模糊情感本体框架的构建及其应用
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摘要
图像检索一直以来都是众多学者的研究内容之一,发展至今,大致可以分为四种类型,有基于内容的图像检索,有基于图像语义的检索,有基于反馈的信息检索技术,还有基于知识的信息检索系统。然而目前为止,并未有研究者将模糊理论应用到图像检索中,而本文实现了将模糊概念格理论应用于图像情感语义检索的过程,且由分析可知,这是可行的。由于图像所包含的情感是丰富的,不确定的,故将模糊形式概念分析理论应用于情感本体的构建也是合理的,从而更好的实现用户情感的检索。
本文在同课题组的研究基础上查阅大量中外文献,基于MPEG-7的图像标准,提出了模糊情感本体框架;接着在模糊情感的基础上,将模糊概念格理论应用到情感本体库的构建中,设计了模糊情感概念格;并且在语义情感方面使用了基于VAD/PAD和OCC空间的情感分层内容,从而使得最终的情感检索能更好的实现用户的需求。最后在这些研究内容的基础上,提出了一个图像情感语义检索模型,并通过实验,对模型的可行性及有效性进行了验证。本文所做工作对情感本体库的研究做了有益的尝试。
本文所做工作如下:
(1)应用模糊形式概念分析方法构建了模糊情感概念格。
该概念格是基于模糊形式概念分析方法的。模糊形式概念分析方法可以很好的用于情感本体的抽取,而抽取出的情感本体又可用于后续的研究。
(2)设计了一个模糊情感匹配规则。
这是一个可以用于模糊情感推理的匹配规则,算法所提取出的情感规则是模糊的,可以用于情感的模糊推理,从而得出一定的情感规则,用于后续的情感检索过程。
(3)基于MPEG-7在情感方面的标准以及模糊概念格,构建了模糊情感本体框架。
(4)基于模糊情感本体,设计了一个图像情感语义检索模型。该模型提供了情感语义在实际操作中的检索流程,为之后的程序设计提供基础。
Image retrieval has been a hot topic for a great mount of researchers. Up to the present, there are about four kinds of image retrieve, which are content-based, image semantic-based, feedback-based and knowledge-based. However, no one use the fuzzy theory in the image searching. This paper basically implements it which is applying the concept lattice theory to the image retrieval process of emotional semantics, and the analysis shows that it is feasible.
As the image contains a wealth of emotions and is uncertain, it is reasonable that the fuzzy Formal Concept Analysis theory is applied to build the emotion ontology. So that we can get a better search results for the user’s emotional retrieve.
On the basis of our research group’s work and a large number of Chinese and foreign literature, the paper proposes a fuzzy ontology frame of emotion which is based on the standard of MPEG-7.Then, the paper applies the fuzzy concept lattice theory to the construction procedure of emotional ontology library, which is on the basis of the fuzzy affection, and designs a fuzzy emotion concept lattice. And the paper uses the layered emotion contents of VAD/PAD and OCC in the selecting of semantic affection space which can make the user be more satisfied with the final image search results.
Finally, the paper presents a semantic image retrieval model on the basis of the previous studies and an experiment is completed to verify the feasibility and effectiveness of the model. And it is also a good attempt to the research of the emotional ontology library.
The work we do list as follows:
(1) The fuzzy emotion concept lattice by using the Fuzzy Formal Concept Analysis (FFCA) is constructed.
The emotion concept lattice is based on the FFCA method. The FFCA method can be very good for the extraction of emotional ontology, and the extracted emotional ontology can also be used for subsequent studies
(2) An algorithm of fuzzy emotional mapping rules is designed.
This is a matching algorithm that can be used for the fuzzy emotional reasoning. The rules that be extracted by the algorithm are fuzzy and can be used for emotional fuzzy reasoning. Then the emotional mapping rules got by the reasoning process can be used for the subsequent retrieval process of emotion.
(3) A fuzzy emotional ontology frame is built based on the relevant standard of MPEG-7 and fuzzy concept lattice.
(4) A semantic retrieval model of image is designed on the basis of frame of fuzzy emotional ontology.
The model provides the retrieval process in the actual operation and it can be a basis for the design of subsequent program.
本文在同课题组的研究基础上查阅大量中外文献,基于MPEG-7的图像标准,提出了模糊情感本体框架;接着在模糊情感的基础上,将模糊概念格理论应用到情感本体库的构建中,设计了模糊情感概念格;并且在语义情感方面使用了基于VAD/PAD和OCC空间的情感分层内容,从而使得最终的情感检索能更好的实现用户的需求。最后在这些研究内容的基础上,提出了一个图像情感语义检索模型,并通过实验,对模型的可行性及有效性进行了验证。本文所做工作对情感本体库的研究做了有益的尝试。
本文所做工作如下:
(1)应用模糊形式概念分析方法构建了模糊情感概念格。
该概念格是基于模糊形式概念分析方法的。模糊形式概念分析方法可以很好的用于情感本体的抽取,而抽取出的情感本体又可用于后续的研究。
(2)设计了一个模糊情感匹配规则。
这是一个可以用于模糊情感推理的匹配规则,算法所提取出的情感规则是模糊的,可以用于情感的模糊推理,从而得出一定的情感规则,用于后续的情感检索过程。
(3)基于MPEG-7在情感方面的标准以及模糊概念格,构建了模糊情感本体框架。
(4)基于模糊情感本体,设计了一个图像情感语义检索模型。该模型提供了情感语义在实际操作中的检索流程,为之后的程序设计提供基础。
Image retrieval has been a hot topic for a great mount of researchers. Up to the present, there are about four kinds of image retrieve, which are content-based, image semantic-based, feedback-based and knowledge-based. However, no one use the fuzzy theory in the image searching. This paper basically implements it which is applying the concept lattice theory to the image retrieval process of emotional semantics, and the analysis shows that it is feasible.
As the image contains a wealth of emotions and is uncertain, it is reasonable that the fuzzy Formal Concept Analysis theory is applied to build the emotion ontology. So that we can get a better search results for the user’s emotional retrieve.
On the basis of our research group’s work and a large number of Chinese and foreign literature, the paper proposes a fuzzy ontology frame of emotion which is based on the standard of MPEG-7.Then, the paper applies the fuzzy concept lattice theory to the construction procedure of emotional ontology library, which is on the basis of the fuzzy affection, and designs a fuzzy emotion concept lattice. And the paper uses the layered emotion contents of VAD/PAD and OCC in the selecting of semantic affection space which can make the user be more satisfied with the final image search results.
Finally, the paper presents a semantic image retrieval model on the basis of the previous studies and an experiment is completed to verify the feasibility and effectiveness of the model. And it is also a good attempt to the research of the emotional ontology library.
The work we do list as follows:
(1) The fuzzy emotion concept lattice by using the Fuzzy Formal Concept Analysis (FFCA) is constructed.
The emotion concept lattice is based on the FFCA method. The FFCA method can be very good for the extraction of emotional ontology, and the extracted emotional ontology can also be used for subsequent studies
(2) An algorithm of fuzzy emotional mapping rules is designed.
This is a matching algorithm that can be used for the fuzzy emotional reasoning. The rules that be extracted by the algorithm are fuzzy and can be used for emotional fuzzy reasoning. Then the emotional mapping rules got by the reasoning process can be used for the subsequent retrieval process of emotion.
(3) A fuzzy emotional ontology frame is built based on the relevant standard of MPEG-7 and fuzzy concept lattice.
(4) A semantic retrieval model of image is designed on the basis of frame of fuzzy emotional ontology.
The model provides the retrieval process in the actual operation and it can be a basis for the design of subsequent program.
引文
[1] Sumedha Kshirsagar. A Multilayer Personality Model[C]. Proceedings of the 2nd international symposium on Smart graphics table of contents,Hawthorne, New York, 2002:107-115.
[2]谷学静.基于HMM的人工心理建模方法及虚拟人相关技术研究[D].北京:北京科技大学, 2003.
[3]魏哲华.基于人工心理理论的情感机器人的情感计算研究[D].北京:北京科技大学,2002.
[4]徐琳宏,林鸿飞,潘宇等.情感词汇本体的构造[J] .情报学报,2008,27(2):186-190.
[5] Vasileios Mezaris, Ioannis Kompatsiaris, Michael G. Strintzis. An ontology approach to object-based image retrieval[C]. In Proc. IEEE Int. Conf. on Image Processing (ICIP), 2003.
[6] Vasileios Mezaris, Ioannis Kompatsiaris, Michael G. Strintzis. Region-Based Image Retrieval Using an Object Ontology and Relevance Feedback [J]. EURASIP Journal on Applied Signal Processing, 2004,6:886–901.
[7]陈颖,刘明政.基于本体技术的内容图像检索系统[J] .计算机与数字工程,2007,40(8):111-113.
[8]吕永帅,秦勃.基于本体的图像检索研究[J].微计算机信息,2008,24(3):294-295.
[9]孙亮,任小康.基于本体的图像语义检索模型[J].重庆工学院学报(自然科学),2009,23(1) :127-131.
[10] Obitko M, SnaseI V,Smid J.Ontology Design with Formal Concept Analysis[C]. In:Vaelav Snasel,Radim Belohlavek,eds. Proceedings of the CLA 2004 International Workshop on Concept Lattices and their Applications Ostrava.Czech Republic,2004.
[11] Haav Hele-Mai.A Semi-automatic Method to Ontology Design by Using FCA [C]. In:Vadav Snasel,Radim Belohlavek,eds. Proceedings of the CLA 2004 International workshop on Concept Lattices and their Applications Ostrava,Czech Republic,2004.
[12] Cimiano P,StiJmme G,Hotho A, Tane J.Conceptual Knowledge Processing with Formal Concept Analysis and Ontologies[C].In:Proceedings of the Second International Conference on Formal Concept Analysis(ICFCA 04) .2004.189～207.
[13] Quan T T,Hui S C,Fong Alvis C M,et a1.Automatic Generation of Ontology for Scholarly Semantic web . In : Proc . of the 3rd International Semantic Web Conference(1SWI :2004.Novemher7-11.Hiroshima.Japan). LNAl 3298,Springer-Verlag,2004.726-740.
[14]周文,刘宗田,强宇等.基于模糊FCA的本体聚类生成算法[J].计算机科学,2006,33(11):101-103.
[15]焦立鹏.基于模糊近似推理的图像情感注释[D].太原:太原理工大学,2007.
[16] Chan M K, Ada F, Man H W. Mining fuzzy association rules in database[C], In: Proceedings of the internationa1Conefrence on information and knowledge Management,Las Vegas,Nevda,1997, 10-14.
[17] Lu Jianjiang, Qian Zuoping, Song Ziling. Application of normal cloud association rules on prediction[J],Journal of Computer Research and Development,2000,37(11):.1317-1320 (in Chinese).
[18] Lu Jianjiang , Qian ZuoPing ,SongZiling,Mining linguist Valued associations[J],Journal.
[19] A. W. Fu,M. H. Wong, S.C.Sze, etc. Finding fuzzy sets for the mining of fuzzy association rules for numerical attributes[C]. In proceedings of the first International symposium of Intelligent Data Engineering and Learning(IDEAL98),263-268, l998,10.
[20] C.M.Kuok, A.Fu, M.H. Wong. Mining fuzzy associations in databases. In SIGMOD Record[J] , 1998,41-46.
[21] Oloveira R, T Braun. TCP in wireless Mobile Ad Hoc Networks[R]. Technical Report IAM-02-003, Inst of Computer Science, University of Berne, 2002,07.
[22] T Nandagopal T Kim, X Gao, V Bharghgvan. Achieving MAC Layer Fairness in Wireless Packet Networks [J]. ACM Mobicom, 2000.
[23]强宇,刘宗田,林炜等.一种模糊概念格构造算法研究[J] .计算机工程与应用,2004,40(29) :50-53.
[24]强宇.模糊概念格模型及其应用研究[D].上海:上海大学,2006.
[25]赵涓涓,陈俊杰,刘嘉琳等.以本体为核心的图像情感语义检索模型[J].计算机应用,2009,29(5):1430-1436.
[26]贾秀玲,文敦伟.面向文本的本体学习研究综述[J].计算机科学,2007,34(2):181-185.
[27]顾金睿,王芳.关于本体论的研究综述[J].情报科学,2007,25(6):949-956.
[28] ISO/IEC JTC1/SC29/WG11N6828, MPEG-7 Overview (version 10).
[29]白露,马慧,黄宇霞,罗跃嘉.中国情绪图片系统的编制—在46名中国大学生中的试用[J] .中国心理卫生杂志. 2005,19(11) :719-722.
[30]黄宇霞,罗跃嘉.国际情绪图片系统在中国的试用研究[J] .中国心理卫生杂志. 2004,18(9) :631-634.
[31] Peter J. Lang, Margaret M. Bradley, etc. International Affective Picture System (IAPS): Instruction Manual and Affective Ratings. Cuthbert International Affective Picture System, 2005, University of Florida.
[32] B. S. Manjunath. IEEE International Conference on Consumer Electronics Los Angeles, CA, June 2001,MPEG-7 Visual Descriptors[C].
[33] Digman, J. M., Personality structure: Emergence of the five factor model [J], Annual Review of Psychology, 1990, 41:417-440.
[34] McCrae, R. R., and John, O. P., An introduction to the five factor model and its applications [J]. Special Issue: The five factor model: Issues and applications. Journal of Personality, 1992, 60:175-215.
[35] Samuel D. Gosling, Peter J. Rent frow, and William B. Swann Jr. A very brief measure of the Big-Five personality domains [J]. Journal of Research in Personality, 2003(37):504-528.
[36]钟建安,段锦云. "大五"人格模型及其在工业与组织心理学中的应用[J].心理科学发展,2004,12(4):578-583.
[37] Ortony A, Clore G, Collins A. The Cognitive Structure of Emotions[M]. Cambridge University Press, 1988.
[38]郭尚波.个性化情感建模方法的研究[D].太原:太原理工大学,2008.
[39] Patrick Gebhard. ALML-A Layered Model of Affect[C]. AAMAS’05 July 25-29, Netherlands, ACM. 2005.
[40]王伟凝,余英林.图像的情感语义研究进展[J] .电路与系统学报,2003,8(5):101-109.
[41]胡明涵,张俐,任飞亮.模糊形式概念分析与模糊概念格[J].东北大学学报(自然科学版). 2007,28(9):1274-1277.
[42]黄健斌,姬红兵.基于模糊概念格的Web搜索结果聚类算法[J] .西安电子科技大学学报(自然科学版). 2005,32(6):856-860.
[43]强宇,刘宗田,吴强等.模糊概念格在知识发现中的应用研究[J].计算机科学. 2005, 32(1):182-184.
[44]孙凌宇,冷明,彭宣戈.基于对象和属性交叉渐进模糊概念格生成算法[J].计算机工程与应用. 2008, 44(21):202-205.
[45]周文,刘宗田,陈慧琼. FCA与本体结合研究的综述[J].计算机科学,2006,33(2):8-12.
[46] Marek Obitko, Vaclav Snasel, Jan Smid. Ontology Design with Formal Concept Analysis [J], 2004, 111-119.
[47]宋亦旭,贾培发.人工情感及其应用[J].控制理论与应用,2004,21(2):315-320.
[48]向友君,谢胜利.图像检索技术综述[J].重庆邮电学院学报(自然科学版),2006,18(3):1-7.
[49]王上飞,薛佳,王煦法.基于内容的情感图像获取模型[J] .计算机科学,2004,31(9):186-190.
[2]谷学静.基于HMM的人工心理建模方法及虚拟人相关技术研究[D].北京:北京科技大学, 2003.
[3]魏哲华.基于人工心理理论的情感机器人的情感计算研究[D].北京:北京科技大学,2002.
[4]徐琳宏,林鸿飞,潘宇等.情感词汇本体的构造[J] .情报学报,2008,27(2):186-190.
[5] Vasileios Mezaris, Ioannis Kompatsiaris, Michael G. Strintzis. An ontology approach to object-based image retrieval[C]. In Proc. IEEE Int. Conf. on Image Processing (ICIP), 2003.
[6] Vasileios Mezaris, Ioannis Kompatsiaris, Michael G. Strintzis. Region-Based Image Retrieval Using an Object Ontology and Relevance Feedback [J]. EURASIP Journal on Applied Signal Processing, 2004,6:886–901.
[7]陈颖,刘明政.基于本体技术的内容图像检索系统[J] .计算机与数字工程,2007,40(8):111-113.
[8]吕永帅,秦勃.基于本体的图像检索研究[J].微计算机信息,2008,24(3):294-295.
[9]孙亮,任小康.基于本体的图像语义检索模型[J].重庆工学院学报(自然科学),2009,23(1) :127-131.
[10] Obitko M, SnaseI V,Smid J.Ontology Design with Formal Concept Analysis[C]. In:Vaelav Snasel,Radim Belohlavek,eds. Proceedings of the CLA 2004 International Workshop on Concept Lattices and their Applications Ostrava.Czech Republic,2004.
[11] Haav Hele-Mai.A Semi-automatic Method to Ontology Design by Using FCA [C]. In:Vadav Snasel,Radim Belohlavek,eds. Proceedings of the CLA 2004 International workshop on Concept Lattices and their Applications Ostrava,Czech Republic,2004.
[12] Cimiano P,StiJmme G,Hotho A, Tane J.Conceptual Knowledge Processing with Formal Concept Analysis and Ontologies[C].In:Proceedings of the Second International Conference on Formal Concept Analysis(ICFCA 04) .2004.189～207.
[13] Quan T T,Hui S C,Fong Alvis C M,et a1.Automatic Generation of Ontology for Scholarly Semantic web . In : Proc . of the 3rd International Semantic Web Conference(1SWI :2004.Novemher7-11.Hiroshima.Japan). LNAl 3298,Springer-Verlag,2004.726-740.
[14]周文,刘宗田,强宇等.基于模糊FCA的本体聚类生成算法[J].计算机科学,2006,33(11):101-103.
[15]焦立鹏.基于模糊近似推理的图像情感注释[D].太原:太原理工大学,2007.
[16] Chan M K, Ada F, Man H W. Mining fuzzy association rules in database[C], In: Proceedings of the internationa1Conefrence on information and knowledge Management,Las Vegas,Nevda,1997, 10-14.
[17] Lu Jianjiang, Qian Zuoping, Song Ziling. Application of normal cloud association rules on prediction[J],Journal of Computer Research and Development,2000,37(11):.1317-1320 (in Chinese).
[18] Lu Jianjiang , Qian ZuoPing ,SongZiling,Mining linguist Valued associations[J],Journal.
[19] A. W. Fu,M. H. Wong, S.C.Sze, etc. Finding fuzzy sets for the mining of fuzzy association rules for numerical attributes[C]. In proceedings of the first International symposium of Intelligent Data Engineering and Learning(IDEAL98),263-268, l998,10.
[20] C.M.Kuok, A.Fu, M.H. Wong. Mining fuzzy associations in databases. In SIGMOD Record[J] , 1998,41-46.
[21] Oloveira R, T Braun. TCP in wireless Mobile Ad Hoc Networks[R]. Technical Report IAM-02-003, Inst of Computer Science, University of Berne, 2002,07.
[22] T Nandagopal T Kim, X Gao, V Bharghgvan. Achieving MAC Layer Fairness in Wireless Packet Networks [J]. ACM Mobicom, 2000.
[23]强宇,刘宗田,林炜等.一种模糊概念格构造算法研究[J] .计算机工程与应用,2004,40(29) :50-53.
[24]强宇.模糊概念格模型及其应用研究[D].上海:上海大学,2006.
[25]赵涓涓,陈俊杰,刘嘉琳等.以本体为核心的图像情感语义检索模型[J].计算机应用,2009,29(5):1430-1436.
[26]贾秀玲,文敦伟.面向文本的本体学习研究综述[J].计算机科学,2007,34(2):181-185.
[27]顾金睿,王芳.关于本体论的研究综述[J].情报科学,2007,25(6):949-956.
[28] ISO/IEC JTC1/SC29/WG11N6828, MPEG-7 Overview (version 10).
[29]白露,马慧,黄宇霞,罗跃嘉.中国情绪图片系统的编制—在46名中国大学生中的试用[J] .中国心理卫生杂志. 2005,19(11) :719-722.
[30]黄宇霞,罗跃嘉.国际情绪图片系统在中国的试用研究[J] .中国心理卫生杂志. 2004,18(9) :631-634.
[31] Peter J. Lang, Margaret M. Bradley, etc. International Affective Picture System (IAPS): Instruction Manual and Affective Ratings. Cuthbert International Affective Picture System, 2005, University of Florida.
[32] B. S. Manjunath. IEEE International Conference on Consumer Electronics Los Angeles, CA, June 2001,MPEG-7 Visual Descriptors[C].
[33] Digman, J. M., Personality structure: Emergence of the five factor model [J], Annual Review of Psychology, 1990, 41:417-440.
[34] McCrae, R. R., and John, O. P., An introduction to the five factor model and its applications [J]. Special Issue: The five factor model: Issues and applications. Journal of Personality, 1992, 60:175-215.
[35] Samuel D. Gosling, Peter J. Rent frow, and William B. Swann Jr. A very brief measure of the Big-Five personality domains [J]. Journal of Research in Personality, 2003(37):504-528.
[36]钟建安,段锦云. "大五"人格模型及其在工业与组织心理学中的应用[J].心理科学发展,2004,12(4):578-583.
[37] Ortony A, Clore G, Collins A. The Cognitive Structure of Emotions[M]. Cambridge University Press, 1988.
[38]郭尚波.个性化情感建模方法的研究[D].太原:太原理工大学,2008.
[39] Patrick Gebhard. ALML-A Layered Model of Affect[C]. AAMAS’05 July 25-29, Netherlands, ACM. 2005.
[40]王伟凝,余英林.图像的情感语义研究进展[J] .电路与系统学报,2003,8(5):101-109.
[41]胡明涵,张俐,任飞亮.模糊形式概念分析与模糊概念格[J].东北大学学报(自然科学版). 2007,28(9):1274-1277.
[42]黄健斌,姬红兵.基于模糊概念格的Web搜索结果聚类算法[J] .西安电子科技大学学报(自然科学版). 2005,32(6):856-860.
[43]强宇,刘宗田,吴强等.模糊概念格在知识发现中的应用研究[J].计算机科学. 2005, 32(1):182-184.
[44]孙凌宇,冷明,彭宣戈.基于对象和属性交叉渐进模糊概念格生成算法[J].计算机工程与应用. 2008, 44(21):202-205.
[45]周文,刘宗田,陈慧琼. FCA与本体结合研究的综述[J].计算机科学,2006,33(2):8-12.
[46] Marek Obitko, Vaclav Snasel, Jan Smid. Ontology Design with Formal Concept Analysis [J], 2004, 111-119.
[47]宋亦旭,贾培发.人工情感及其应用[J].控制理论与应用,2004,21(2):315-320.
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