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Learning multi-task local metrics for image annotation

机译：学习用于图像标注的多任务本地指标

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摘要

The goal of image annotation is to automatically assign a set of textual labels to an image to describe the visual contents thereof. Recently, with the rapid increase in the number of web images, nearest neighbor (NN) based methods have become more attractive and have shown exciting results for image annotation. One of the key challenges of these methods is to define an appropriate similarity measure between images for neighbor selection. Several distance metric learning (DML) algorithms derived from traditional image classification problems have been applied to annotation tasks. However, a fundamental limitation of applying DML to image annotation is that it learns a single global distance metric over the entire image collection and measures the distance between image pairs in the image-level. For multi-label annotation problems, it may be more reasonable to measure similarity of image pairs in the label-level. In this paper, we develop a novel label prediction scheme utilizing multiple label-specific local metrics for label-level similarity measure, and propose two different local metric learning methods in a multi-task learning (MTL) framework. Extensive experimental results on two challenging annotation datasets demonstrate that 1) utilizing multiple local distance metrics to learn label-level distances is superior to using a single global metric in label prediction, and 2) the proposed methods using the MTL framework to learn multiple local metrics simultaneously can model the commonalities of labels, thereby facilitating label prediction results to achieve state-of-the-art annotation performance.
机译：图像注释的目的是为图像自动分配一组文本标签，以描述其视觉内容。近来，随着网络图像数量的迅速增加，基于最近邻居（NN）的方法变得越来越有吸引力，并且显示出令人兴奋的图像标注结果。这些方法的主要挑战之一是在图像之间定义适当的相似度度量以进行邻居选择。源自传统图像分类问题的几种距离度量学习（DML）算法已应用于注释任务。但是，将DML应用于图像注释的基本局限性在于，它在整个图像集合中学习单个全局距离度量，并在图像级别测量图像对之间的距离。对于多标签注释问题，在标签级别测量图像对的相似度可能更合理。在本文中，我们开发了一种新颖的标签预测方案，该方案利用多个特定于标签的局部度量进行标签级相似性度量，并在多任务学习（MTL）框架中提出了两种不同的局部度量学习方法。在两个具有挑战性的注释数据集上的大量实验结果表明，1）在标签预测中利用多个局部距离度量学习标签级距离优于使用单个全局度量，以及2）使用MTL框架提出的方法来学习多个局部度量同时可以对标签的共性进行建模，从而有助于标签预测结果实现最新的标注性能。

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