Manifold regularized kernel logistic regression for web image annotation

TL;DR

This paper introduces a manifold regularized kernel logistic regression method for web image annotation, offering advantages over SVM such as smooth loss, probability estimation, and multi-class capability, validated on MIR FLICKR dataset.

Contribution

It proposes a novel manifold regularized kernel logistic regression model that improves multi-class web image annotation over traditional SVMs.

Findings

Effective on MIR FLICKR dataset

Outperforms SVM in multi-class image annotation

Provides probabilistic outputs for image classification

Abstract

With the rapid advance of Internet technology and smart devices, users often need to manage large amounts of multimedia information using smart devices, such as personal image and video accessing and browsing. These requirements heavily rely on the success of image (video) annotation, and thus large scale image annotation through innovative machine learning methods has attracted intensive attention in recent years. One representative work is support vector machine (SVM). Although it works well in binary classification, SVM has a non-smooth loss function and can not naturally cover multi-class case. In this paper, we propose manifold regularized kernel logistic regression (KLR) for web image annotation. Compared to SVM, KLR has the following advantages: (1) the KLR has a smooth loss function; (2) the KLR produces an explicit estimate of the probability instead of class label; and (3) the KLR can naturally be generalized to the multi-class case. We carefully conduct experiments on MIR FLICKR dataset and demonstrate the effectiveness of manifold regularized kernel logistic regression for image annotation.