Manifold regularized kernel logistic regression for web image annotation
This work addresses image annotation for web users managing multimedia, but it is incremental as it modifies an existing method (KLR) with manifold regularization.
The authors tackled the problem of large-scale web image annotation by proposing manifold regularized kernel logistic regression (KLR), which outperforms SVM by offering a smooth loss function, probability estimates, and natural multi-class generalization, with experimental validation on the MIR FLICKR dataset.
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.