Manifold regularized kernel logistic regression for web image annotation
Manifold regularized kernel logistic regression for web image annotation
复制标题
Web 图像标注的流形正则核 Logistic 回归
DOI：
10.1016/j.neucom.2014.06.096
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发表时间：
2016-01-08
期刊：
影响因子：
6
通讯作者：
Lu, Ke
中科院分区：
文献类型：
--
作者：
Liu, Weifeng;Liu, Hongli;Lu, Ke
随着互联网技术和智能设备的快速发展，用户经常需要使用智能设备管理大量的多媒体信息，如个人图像和视频的访问和浏览。这些需求在很大程度上依赖于图像（视频）标注的成功，因此近年来，通过创新的机器学习方法进行大规模图像标注引起了人们的广泛关注。其中一个代表性的工作是支持向量机（SVM）。虽然支持向量机在二值分类中表现良好，但其损失函数不光滑，不能自然地覆盖多类情况。在本文中，我们提出了流形正则化核逻辑回归（KLR）的Web图像标注。与SVM相比，KLR具有以下优点：（1）KLR具有平滑的损失函数;（2）KLR产生概率的显式估计而不是类别标签;（3）KLR可以自然地推广到多类情况。我们仔细地进行了实验MIR FLICKR数据集，并证明了流形正则化核逻辑回归图像标注的有效性。(C)2015 Elsevier B. V.版权所有。
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. (C) 2015 Elsevier B.V. All rights reserved.