Unsupervised and Semi-Supervised Clustering for Large Image Database Indexing and Retrieval

Hien Phuong Lai, M. Visani, A. Boucher, J. Ogier
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引用次数: 6

Abstract

The feature space structuring methods play a very important role in finding information in large image databases. They organize indexed images in order to facilitate, accelerate and improve the results of further retrieval. Clustering, one kind of feature space structuring, may organize the dataset into groups of similar objects without prior knowledge (unsupervised clustering) or with a limited amount of prior knowledge (semi- supervised clustering). In this paper, we present both formal and experimental comparisons of different unsupervised clustering methods for structuring large image databases. We use different image databases of increasing sizes (Wang, PascalVoc2006, Caltech101, Core130k) to study the scalability of the different approaches. Moreover, a summary of semi-supervised clustering methods is presented and an interactive semi-supervised clustering model using the HMRF-kmeans is experimented on the Wang image database in order to analyse the improvement of the clustering results when user feedbacks are provided.
大型图像数据库索引与检索的无监督与半监督聚类
特征空间结构化方法在大型图像数据库的信息查找中起着非常重要的作用。他们组织索引图像是为了方便、加速和改进进一步检索的结果。聚类是一种特征空间结构,它可以在没有先验知识(无监督聚类)或有限先验知识(半监督聚类)的情况下将数据集组织成相似对象的组。在本文中,我们对不同的无监督聚类方法进行了形式化和实验性的比较,以构建大型图像数据库。我们使用不同大小的图像数据库(Wang, PascalVoc2006, Caltech101, Core130k)来研究不同方法的可扩展性。此外,对半监督聚类方法进行了总结,并在Wang图像数据库上实验了一种基于HMRF-kmeans的交互式半监督聚类模型,以分析在提供用户反馈的情况下聚类结果的改进。
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