Determination of multi-modal dimension metric learning with application to web image retrieval

Abstract

Many real-world applications, like multimedia retrieval, confront the difficulty of determining the distance between any two items on Multi-modal data. According to most current Dimension Metrics Learning (DML) techniques, distance metrics may be learned using just one feature type or an aggregated feature space where many features are simply connected. Even though DML has been extensively researched. This study proposes a new framework for online learning, as well as a new classroom learning system for online Multi-modal dimension metric learning (OMDML), that is both efficient and scalable. This paper proposes a low-rank OMDML calculation to reduce the expensive cost of DML on high-dimensional component space, which reduces the computational cost while maintaining extremely competitive or much higher learning accuracy. With the purpose of determining whether or not multi-modular image recovery calculations can be successfully implemented, a large number of experiments are carried out. In most datasets tested, the suggested approach consistently outperforms alternative state-of-the-art algorithms, according to extensive experimental results