SGER Proposal: A "Transderivational" Search Engine for Creative Analogy Generation in Mixed-Media Design

SGER 提案：混合媒体设计中创意类比生成的“转导”搜索引擎

• 批准号: 0742440
• 负责人: Huong Dinh
• 金额: --
• 依托单位: Stevens Institute of Technology
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2007
• 资助国家: 美国
• 起止时间: 2007-09-15 至 2010-02-28
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0742440&HistoricalAwards=false
• 关键词: SGER; Proposal; Transderivational; Search; Engine

This project will develop a transderivational search engine based on the neurological condition known as synaesthesia in which two or more senses are crossed (e.g., when seeing a color causes one to hear a sound) to help people to discover connections between text, 1D audio, 2D image, 3D geometry and 4D motion data. The project is inspired by the ability of artists and designers to find analogies between diverse artifacts and bring them together to compose a coherent and novel narrative.The intellectual merit of this research is the development of matching algorithms that suggest analogies across different media forms by looking at structural similarity within media content. The result will be a transformative technology at the intersection of art, computer graphics, machine learning, cognitive psychology, and human-computer interaction (HCI). Transderivational search will enhance the synaesthetic effect in analogy generation and will naturally lend itself to a wide range of brainstorming pursuits. Finding analogies between media of different forms (e.g., audio and 3D shapes) has not been explored, nor has there been much focus on non-literal search engines. Literal searches rely only on explicit meaning (e.g., the word ?three? and an image of the number 3) and categorization to determine similarity. Instead, this project will compare media samples by looking for structural similarity using analytical approaches such as statistical shape distributions, frequency analysis, and machine learning techniques to discover relationships between mixed- (multi-dimensional) media samples.The broader impacts of this research are in advancing artificial intelligence through transderivational search (essential to language and cognitive processing) and in opening up new research questions on search technology. The educational impacts are in drawing more women and minorities into CS and improving retention in CS programs by showing the relevance of search technology to creative design and to multimedia management. The transderivational search tools will be used by students in introductory level CS courses to build basic media management software. Transderivational search can also serve as a testbed for exploring algorithms in high level CS courses on machine learning, computer vision and graphics.

这个项目将开发一个基于神经系统条件的transderivational搜索引擎，这种神经系统条件被称为通感，其中两个或多个感官交叉（例如，当看到一种颜色时，会听到一种声音），帮助人们发现文本、1D音频、2D图像、3D几何图形和4D运动数据之间的联系。该项目的灵感来自于艺术家和设计师在不同的人工制品之间找到类比，并将它们放在一起组成一个连贯而新颖的叙事的能力。这项研究的智力价值是匹配算法的发展，通过查看媒体内容中的结构相似性，在不同的媒体形式中提出类比。其结果将是艺术、计算机图形学、机器学习、认知心理学和人机交互（HCI）交叉的变革性技术。转衍搜索将增强类比生成中的通感效应，并自然地适用于广泛的头脑风暴追求。在不同形式的媒体之间寻找类比（例如，音频和3D形状）还没有被探索，也没有太多的关注非文字搜索引擎。文字搜索仅依赖于明确的含义（例如，这个词？三个？和数字3的图像）和分类以确定相似性。相反，该项目将通过使用统计形状分布，频率分析，和机器学习技术来发现混合-（多维）媒体样本。这项研究的更广泛的影响是通过转衍搜索推进人工智能（对语言和认知处理至关重要），并开辟了搜索技术的新研究问题。教育的影响是吸引更多的妇女和少数民族进入CS和提高保留CS程序显示搜索技术的相关性，创造性的设计和多媒体管理。在CS入门级课程中，学生将使用transderivational搜索工具来构建基本的媒体管理软件。Transderivational search也可以作为在机器学习、计算机视觉和图形学的高级CS课程中探索算法的测试平台。