Signature Quadratic Form Distance for Efficient Multimedia Database Retrieval

签名二次形式距离是一种自适应的基于距离的相似性度量，用于比较多媒体数据的灵活特征表示。该相似性度量是二次型距离的推广，并且能够计算不同结构和大小的特征表示之间的相似性值。以这种方式，它是适用于复杂的多媒体数据，而且与其他自适应的基于距离的相似性措施的质量相媲美。在这个项目的范围内，我们打算调查和提高签名二次形式距离在基于内容的多媒体检索领域的适用性和效率。我们计划开发一种新的模型的签名二次形式的距离，这是能够比较离散以及连续的多媒体数据的特征分布。基于这种新的距离模型，我们计划调查和扩展的签名二次形式距离袋的视觉词的方法，以增加后者的灵活性的适用性。为了有效地处理相似性查询，我们最终计划近似离散和连续的特征分布以及距离计算。上述主题的研究结果将扩大的适用性和提高效率的签名二次形式的距离被利用在更复杂的设置，基于内容的多媒体检索。

The Signature Quadratic Form Distance is an adaptive distance-based similarity measure for the comparison of exible feature representations of multimedia data. This similarity measure is a generalization of the Quadratic Form Distance and enables the computation of similarity values among feature representations of different structure and size. In this way, it is applicable to complex multimedia data and furthermore comparable with the quality of other adaptive distance-based similarity measures. Within the scope of this project, we intend to investigate and increase the applicability and efficiency of the Signature Quadratic Form Distance in the field of content-based multimedia retrieval. We plan to develop a novel model of the Signature Quadratic Form Distance which is able to compare discrete as well as continuous feature distributions of multimedia data. Based on this novel distance model, we plan to investigate and extend the applicability of the Signature Quadratic Form Distance regarding bag-of-visual- word approaches in order to increase the latter's exibility. In order to process similarity queries efficiently, we finally plan to approximate discrete and continuous feature distributions as well as distance computations. Findings of the aforementioned topics will expand the applicability and improve the efficiency of the Signature Quadratic Form Distance to be leveraged in more complex settings regarding content-based multimedia retrieval.