Approximating the Signature Quadratic Form Distance Using Scalable Feature Signatures

Abstract

The feature signatures in connection with the signature quadratic form distance have become a respected similarity model for effective multimedia retrieval. However, the efficiency of the model is still a challenging task because the signature quadratic form distance has quadratic time complexity according to the number of tuples in feature signatures. In order to reduce the number of tuples in feature signatures, we introduce the scalable feature signatures, a new formal framework based on hierarchical clustering enabling definition of various feature signature reduction techniques. As an example, we use the framework to define a new feature signature reduction technique based on joining of the tuples. We experimentally demonstrate our new feature signature reduction technique can be used to implement more efficient yet effective filter distances approximating the original signature quadratic form distance. We also show the filter distances using our new feature signature reduction technique significantly outperform the filter distances based on the related maximal component feature signatures.

This research has been supported by Czech Science Foundation project GAČR P202/12/P297.