首页> 外文会议>11th ACM International Conference on Multimedia; Nov 4-6, 2003; Berkeley, California, USA >Knowing a Tree from the Forest: Art Image Retrieval using a Society of Profiles
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Knowing a Tree from the Forest: Art Image Retrieval using a Society of Profiles

机译:从森林中了解一棵树:使用个人资料协会的艺术形象检索

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This paper aims to address the problem of art image retrieval (AIR), which aims to help users find their favorite painting images. AIR is of great interests to us because of its application potentials and interesting research challenges―the retrieval is not only based on painting contents or styles, but also heavily based on user preference profiles. This paper describes the collaborative ensemble learning, a novel statistical learning approach to this task. It at first applies probabilistic support vector machines (SVMs) to model each individual user's profile based on given examples, i.e. liked or disliked paintings. Due to the high complexity of profile modelling, the SVMs can be rather weak in predicting preferences for new paintings. To overcome this problem, we combine a society of users' profiles, represented by their respective SVM models, to predict a given user's preferences for painting images. We demonstrate that the combination scheme is embedded in a Bayesian framework and retains intuitive interpretations―like-minded users are likely to share similar preferences. We report extensive empirical studies based on two experimental settings. The first one includes some controlled simulations performed on 4533 painting images. In the second setting, we report evaluations based on user preferences collected through an online web-based survey. Both experiments demonstrate that the proposed approach achieves excellent performance in terms of capturing a user's diverse preferences.
机译:本文旨在解决艺术图像检索(AIR)问题,该问题旨在帮助用户找到自己喜欢的绘画图像。由于AIR的应用潜力和有趣的研究挑战,AIR对我们非常感兴趣-检索不仅基于绘画内容或样式,而且很大程度上基于用户喜好配置文件。本文介绍了协作集成学习,这是一种新颖的统计学习方法。首先,它基于给定的示例(即喜欢或不喜欢的绘画)应用概率支持向量机(SVM)对每个用户的个人资料进行建模。由于轮廓建模的高度复杂性,SVM在预测新绘画的偏好方面可能相当薄弱。为了克服这个问题,我们结合了由用户各自的SVM模型代表的用户个人资料协会,以预测给定用户对绘画图像的偏好。我们证明了组合方案已嵌入贝叶斯框架中并保留了直观的解释-志趣相投的用户可能会分享相似的偏好。我们报告了基于两个实验设置的广泛的经验研究。第一个包括对4533个绘画图像执行的一些受控模拟。在第二种设置中,我们报告基于通过在线网络调查收集的用户偏好的评估。这两个实验都表明,该方法在捕获用户的各种偏好方面取得了出色的性能。

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