Adaptable Distance Functions for Similarity-based Multimedia Retrieval
Adaptable Distance Functions for Similarity-based Multimedia Retrieval
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基于相似性的多媒体检索的自适应距离函数
DOI：
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发表时间：
2006
期刊：
影响因子：
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通讯作者：
T. Seidl
中科院分区：
文献类型：
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作者：
I. Assent;Marc Wichterich;T. Seidl
今天的大量存储加上几乎所有科学或商业应用中的数字技术，如医学和生物成像或音乐档案，处理存储在大型多媒体数据库中的大量图像、视频或音频文件。对于基于内容的数据挖掘和多媒体检索，合适的相似度模型至关重要。适应性距离函数特别适合于匹配人类对相似性的感知。引入二次型（QF）来捕捉特征间相似性的概念，这将它们与传统的逐特征度量（例如欧几里得或曼哈顿不相似函数）区分开来。在计算机视觉中，为了更好地接近人类感知相似性，采用了大地移动距离（Earth Mover’s Distance， EMD），允许在一些限制条件下进行特征变换。在回顾了数据库中基于距离的相似性搜索的概念之后，我们让读者熟悉二次型和EMD背后的灵活基石。这些使得它们能够应用于各种各样的多媒体检索问题。不幸的是，灵活性是有代价的。它们的计算相对耗时，这严重限制了其在交互式多媒体数据库场景中的应用。因此，我们研究了加快检索过程的方法，并展示了一些令人鼓舞的最新结果，通过基于新的下界近似技术的索引支持多步算法来实现这一目标。
Today’s abundance of storage coupled with digital technologies in virtually all scientific or commercial applications such as medical and biological imaging or music archives deal with tremendous quantities of images, videos or audio files stored in large multimedia databases. For content-based data mining and multimedia retrieval purposes, suitable similarity models are crucial. Adaptable distance functions are particularly well-suited to match the human perception of similarity. Quadratic Forms (QF) were introduced to capture the notion of inter-feature similarity which sets them apart from the more traditional feature-by-feature measures from e.g. the Euclidean or Manhattan dissimilarity functions. The Earth Mover’s Distance (EMD) was adopted in Computer Vision to better approach human perceptual similarities by allowing feature transformation under a number of restrictions. After recapping the concepts of distancebased similarity search in databases, we familiarize the reader with the flexible building stones behind Quadratic Forms and the EMD. These enable their application to a large variety of multimedia retrieval problems. Unfortunately, the flexibility comes at a cost. Their computation is relatively time-consuming, which severely limits its adoption in interactive multimedia database scenarios. Therefore, we research methods to speed up the retrieval process and show some encouraging recent results to achieve just that via an index-supported multistep algorithm based on new lower bounding approximation techniques.