Similarity Measure Learning in Closed-Form Solution for Image Classification

Abstract EN

Adopting a measure is essential in many multimedia applications.

Recently, distance learning is becoming an active research problem.

In fact, the distance is the natural measure for dissimilarity.

Generally, a pairwise relationship between two objects in learning tasks includes two aspects: similarity and dissimilarity.

The similarity measure provides different information for pairwise relationships.

However, similarity learning has been paid less attention in learning problems.

In this work, firstly, we propose a general framework for similarity measure learning (SML).

Additionally, we define a generalized type of correlation as a similarity measure.

By a set of parameters, generalized correlation provides flexibility for learning tasks.

Based on this similarity measure, we present a specific algorithm under the SML framework, called correlation similarity measure learning (CSML), to learn a parameterized similarity measure over input space.

A nonlinear extension version of CSML, kernel CSML, is also proposed.

Particularly, we give a closed-form solution avoiding iterative search for a local optimal solution in the high-dimensional space as the previous work did.

Finally, classification experiments have been performed on face databases and a handwritten digits database to demonstrate the efficiency and reliability of CSML and KCSML.