similarity matrix compression for efficient signature quadratic form distance computation
Determining similarities among multimedia objects is a fundamental task in many content-based retrieval, analysis, mining, and exploration applications. Among state-of-the-art similarity measures, the Signature Quadratic Form Distance has shown good applicability and high quality in comparing flexible feature representations. In order to improve the efficiency of the Signature Quadratic Form Distance, we propose the similarity matrix compression approach which aims at compressing the distance's inherent similarity matrix. We theoretically show how to reduce the complexity of distance computations and benchmark computation time improvements. As a result, we improve the efficiency of a single distance computation by a factor up to 9.
similarity matrix compression signature quadratic form distance content-based multimedia retrieval efficient query processing similarity search