Image retrieval using the extended salient region
Image retrieval using the extended salient region
复制标题
使用扩展显着区域的图像检索
DOI：
10.1016/j.ins.2017.03.005
复制
发表时间：
2017
影响因子：
8.1
通讯作者：
Yubo Yuan
中科院分区：
文献类型：
--
作者：
Jing Zhang;Shengwei Feng;Da Li;Yongwei Gao;Zhihua Chen;Yubo Yuan
显著区域是图像中最重要的部分。当人们在大规模数据集中搜索图像时，图像中的显著部分也吸引了最多的注意力。然而，为了提高图像检索的准确性，只考虑图像中最显著的对象是不够的，因为背景也影响图像检索的准确性。为了解决这个问题，本文提出了一个新的概念，称为扩展显着区域（ESR）。首先，使用区域对比度（RC）算法检测输入图像的显著区域。然后，建立极坐标系，以凸极区域的质心为极点。接下来，围绕显著区域的区域由沿逆时针方向移动的相邻区域确定。所得到的突出区域及其周围区域的组合被定义为ESR。我们使用基于Gabor、SIFT和HSVH特征的著名的词袋（BoW）模型从ESR中提取视觉内容，并提出了视觉内容节点的图模型来表示输入图像。然后，我们设计了一种新的算法来执行两个图像之间的匹配。我们还定义了一个新的相似性度量相结合的显着区域和周围地区使用权重的相似性。最后，为了更好地评价图像检索的准确性，提出了一种改进的度量方法--均值标签平均精度（MPERS）。在Corel、TU达姆施塔特和Caltech 101三个基准数据集上的实验结果表明，本文提出的ESR模型和区域匹配算法在图像检索方面具有很高的效率，并且能够获得比现有方法更精确的查询结果。
The salient region is the most important part of an image. The salient portion in images also attracts the most attention when people search for images in large-scale datasets. However, to improve image retrieval accuracy, considering only the most salient object in an image is insufficient because the background also influences the accuracy of image retrieval. To address this issue, this paper proposes a novel concept called the extended salient region (ESR). First, the salient region of an input image is detected using a Region Contrast (RC) algorithm. Then, a polar coordinate system is constructed; the centroid of the salient region is set as the pole. Next, the regions surrounding the salient region are determined by the neighboring regions, moving in a counterclockwise direction. The resulting combination of the salient region and its surrounding regions is defined as the ESR. We extract the visual content from the ESR using the well-known Bag of Words (BoW) model based on Gabor, SIFT and HSVH features and propose a graph model of the visual content nodes to represent the input image. Then, we design a novel algorithm to perform matching between two images. We also define a new similarity measure by combining the similarities of the salient region and the surrounding regions using weights. Finally, to better evaluate the image retrieval accuracy, an improved measure called the mean label average precision (MLAP) is proposed. The results of experiments on three benchmark datasets (Corel, TU Darmstadt, and Caltech 101) demonstrate that our proposed ESR model and region-matching algorithm are highly effective at image retrieval, and can achieve more accurate query results than current state-of-the-art methods.