II-20：图像集合的智能和实用分析分类
II-20: Intelligent and pragmatic analytic categorization of image collections
摘要：
我们介绍II-20（Image Insight 2020），一种用于图像集合分析分类的多媒体分析方法。图像集合的高级可视化已经存在，但它们需要与机器模型紧密集成，以支持分析分类。直接采用计算机视觉和交互式学习技术的搜索。然而，分析分类不是机器分类（两者之间的区别被称为语用差距）：人类在飞行中添加/重新定义/删除相关性类别以建立洞察力，而机器分类器是刚性和非自适应的。分析分类，使用户的洞察力需要一个灵活的机器模型，允许动态滑动的探索搜索轴，以及语义交互。II-20为图像收集的多媒体分析和缩小实用差距带来了3个主要贡献。首先，一个机器模型，密切关注用户的交互，并动态地模拟她的相关性类别。II-20的模型，除了匹配和超过最先进的W。R. t.相关性允许用户在探索-搜索轴上动态滑动，而无需来自她一侧的附加输入。第二，动态的，一次一个图像的俄罗斯方块隐喻与模型协同作用。它允许模型自己分析集合，与用户的交互最少，并补充了经典的网格隐喻。第三，允许用户利用模型来快速扩展（“快进”）相关性类别的快进交互扩展了多媒体分析语义交互字典。自动化实验表明，II-20的模型优于最先进的，也证明了俄罗斯方块的分析质量。用户研究证实，II-20是一个直观，高效，有效的多媒体分析工具。
英文摘要：
We introduce II-20 (Image Insight 2020), a multimedia analytics approach for analytic categorization of image collections. Advanced visualizations for image collections exist, but they need tight integration with a machine model to support analytic categorization. Directly employing computer vision and interactive learning techniques gravitates towards search. Analytic categorization, however, is not machine classification (the difference between the two is called the pragmatic gap): a human adds/redefines/deletes categories of relevance on the fly to build insight, whereas the machine classifier is rigid and non-adaptive. Analytic categorization that brings the user to insight requires a flexible machine model that allows dynamic sliding on the exploration-search axis, as well as semantic interactions. II-20 brings 3 major contributions to multimedia analytics on image collections and towards closing the pragmatic gap. Firstly, a machine model that closely follows the user's interactions and dynamically models her categories of relevance. II-20's model, in addition to matching and exceeding the state of the art w. r. t. relevance, allows the user to dynamically slide on the exploration-search axis without additional input from her side. Secondly, the dynamic, 1-image-at-a-time Tetris metaphor that synergizes with the model. It allows the model to analyze the collection by itself with minimal interaction from the user and complements the classic grid metaphor. Thirdly, the fast-forward interaction, allowing the user to harness the model to quickly expand ("fast-forward") the categories of relevance, expands the multimedia analytics semantic interaction dictionary. Automated experiments show that II-20's model outperforms the state of the art and also demonstrate Tetris's analytic quality. User studies confirm that II-20 is an intuitive, efficient, and effective multimedia analytics tool.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
