Automatic image annotation and retrieval using weighted feature selection
Automatic image annotation and retrieval using weighted feature selection
复制标题
使用加权特征选择自动图像注释和检索
DOI：
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发表时间：
2004
期刊：
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通讯作者：
L. Khan
中科院分区：
文献类型：
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作者：
Lei Wang;L. Khan
技术的发展产生了大量的非文本信息，如图像。一个高效的图像标注和检索系统是非常需要的。聚类算法使得用有限的符号来表示图像的视觉特征成为可能。在此基础上，许多统计模型，分析视觉特征和词之间的对应关系，发现隐藏的语义，已经公布。这些模型改进了大型图像数据库的注释和检索。然而，图像数据通常具有大量维度。传统的聚类算法给这些维度赋予相等的权重，在处理这些维度的过程中变得混乱。在本文中，我们提出了加权特征选择算法作为解决这个问题。对于给定的聚类，我们基于直方图分析确定相关特征，并为相关特征分配更大的权重，而不是相关性较低的特征。我们已经实现了各种不同的模型来链接视觉令牌与关键字的基础上加权特征选择和不选择的K-means算法的聚类结果，并使用基准数据集的精度，召回率和对应准确性的性能进行评估。结果表明，加权特征选择方法在图像自动标注和检索中优于传统的特征选择方法。
The development of technology generates huge amounts of non-textual information, such as images. An efficient image annotation and retrieval system is highly desired. Clustering algorithms make it possible to represent visual features of images with finite symbols. Based on this, many statistical models, which analyze correspondence between visual features and words and discover hidden semantics, have been published. These models improve the annotation and retrieval of large image databases. However, image data usually have a large number of dimensions. Traditional clustering algorithms assign equal weights to these dimensions, and become confounded in the process of dealing with these dimensions. In this paper, we propose weighted feature selection algorithm as a solution to this problem. For a given cluster, we determine relevant features based on histogram analysis and assign greater weight to relevant features as compared to less relevant features. We have implemented various different models to link visual tokens with keywords based on the clustering results of K-means algorithm with weighted feature selection and without feature selection, and evaluated performance using precision, recall and correspondence accuracy using benchmark dataset. The results show that weighted feature selection is better than traditional ones for automatic image annotation and retrieval.