Spatially Aware Enhancement of BoVW-Based Image Retrieval Exploiting a Saliency Map
Spatially Aware Enhancement of BoVW-Based Image Retrieval Exploiting a Saliency Map
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DOI：
10.1007/978-3-319-23117-4_7
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发表时间：
2015-09
期刊：
影响因子：
--
通讯作者：
Z. Zou;H. Koga
中科院分区：
文献类型：
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作者：
Z. Zou;H. Koga
Bag-of-Visual-Words (BoVW) 方案是最流行的相似图像检索方法。传统的 BoVW 将图像建模为局部特征的直方图，其中所有特征都被统一加权。最近，研究人员关注两幅图像前景之间的相似性，因为前景正确地表达了图像的语义。给定图像，这些方法利用从显着性图导出的显着性值来近似局部特征属于前景的可能性。然后构建前景直方图，使得每个局部特征按照其显着性值的比例累积到直方图。最后，通过比较图像的前景直方图来衡量图像之间的相似度。然而，上述策略会低估显着性值较小的局部特征，即使它们位于真实的前景上。本文提出了一种新技术，该技术不忽视这些局部特征并检查它们的空间环境。特别地，当空间周围具有高显着性值时，将高权重分配给具有低显着性值的局部特征，因为该局部特征也可能是前景的一部分。
The Bag-of-Visual-Words (BoVW) scheme is the most popular approach to similar image retrieval. The conventional BoVW models an image as a histogram of local features in which all of the features are uniformly weighed. Recently, researchers have focused on the similarity between the foregrounds of two images, because the foreground properly expresses the semantics of the image. Given an image, these methods approximate the likelihood that a local feature belongs to the foreground with its saliency value derived from the saliency map. The foreground histogram is then constructed in such a way that each local feature is accumulated to the histogram in proportion to its saliency value. Finally, the similarity between images is measured by comparing the foreground histograms of the images. However, the above strategy discounts local features with small saliency values, even if they lie on the genuine foreground. This paper proposes a new technique that does not disregard such local features and examines their spatial surroundings. In particular, a high weight is assigned to a local feature with a low saliency value when the spatial surrounding has a high saliency value, because this local feature is also likely to be a part of the foreground.