Object-based image retrieval with kernel on adjacency matrix and local combined features
Object-based image retrieval with kernel on adjacency matrix and local combined features
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DOI：
10.1145/2379790.2379796
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发表时间：
2012-11
期刊：
影响因子：
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通讯作者：
Heng Qi;Keqiu Li;Yanming Shen;W. Qu
中科院分区：
文献类型：
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作者：
Heng Qi;Keqiu Li;Yanming Shen;W. Qu
在基于对象的图像检索中，有两个重要的问题：一个有效的图像表示方法来表示图像内容和一个有效的图像分类方法来处理用户的反馈，以找到更多的图像包含用户想要的对象类别。在图像表示方法中，基于局部的表示是基于对象的图像检索的最佳选择。支持向量机作为一种基于核的分类方法，在图像分类方面表现出了令人瞩目的性能。但SVM不能工作在基于局部的表示，除非有一个合适的内核。为了解决这个问题，在文献中提出了一些有代表性的核。然而，这些核不能有效地工作在基于对象的图像检索，由于忽略了空间上下文和局部特征的组合。在本文中，我们提出了相邻矩阵（AM）和局部组合特征（LCF），将空间上下文和局部特征的组合纳入内核。我们提出了AM-LCF特征向量来表示图像内容和AM-LCF内核来衡量AM-LCF特征向量之间的相似性。根据详细的分析，我们表明，所提出的核可以克服现有的核的不足。此外，我们通过两个公共图像集上的基于对象的图像检索的实验评估所提出的内核。实验结果表明，该核函数可以提高基于对象的图像检索的性能。
In object-based image retrieval, there are two important issues: an effective image representation method for representing image content and an effective image classification method for processing user feedback to find more images containing the user-desired object categories. In the image representation method, the local-based representation is the best selection for object-based image retrieval. As a kernel-based classification method, Support Vector Machine (SVM) has shown impressive performance on image classification. But SVM cannot work on the local-based representation unless there is an appropriate kernel. To address this problem, some representative kernels are proposed in literatures. However, these kernels cannot work effectively in object-based image retrieval due to ignoring the spatial context and the combination of local features. In this article, we present Adjacent Matrix (AM) and the Local Combined Features (LCF) to incorporate the spatial context and the combination of local features into the kernel. We propose the AM-LCF feature vector to represent image content and the AM-LCF kernel to measure the similarities between AM-LCF feature vectors. According to the detailed analysis, we show that the proposed kernel can overcome the deficiencies of existing kernels. Moreover, we evaluate the proposed kernel through experiments of object-based image retrieval on two public image sets. The experimental results show that the performance of object-based image retrieval can be improved by the proposed kernel.