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計算幾何を用いた高品質イメージ検索システムに関する研究

基于计算几何的高质量图像检索系统研究

基本信息

批准号：
12J07851
负责人：
ガオタントンナスダ
金额：
$ 1.15万
依托单位：
Tohoku University
依托单位国家：
日本
项目类别：
Grant-in-Aid for JSPS Fellows
财政年份：
2012
资助国家：
日本
起止时间：
2012 至 2013
项目状态：
已结题

项目摘要

The objective of this research is to improve the quality of image retrieval in a real-world application to be as high as successful a text retrieval method. We focus on applying shape feature of main objects, which are extracted from a query image, for identifying similarity among images. Two algorithms are proposed. The first one is for improving the time complexity when applying the shape similarity measure. The second algorithm is for improving the quality of the image segmentation when using base-monotone regions. Also to allow the system to be able to automatically locate the important objects.In this research, the shape similarity measure called Modified Hausdorff Distance is applied. Given two set of boundary points P and Q, to compare the two shapes using the Modified Hausdorff Distance, one shape needs to be aligned on the other. The Modified Hausdorff Distance is the average distance of the closest points between the points on the two shape boundary. To obtain an optimal simi … More larity measure, the shapes must be aligned to the most similar part of each other. In a naive method, all pairs of points are applied for finding the optimal transformation. Therefore, the time complexity is cubic in the size of the boundary points.Instead of applying all possible transformation, we proposed a method which applies a pair of correspondence points for mapping the two shapes to the similar part of each other. We also proposed a shape descriptor called a Local Distance Interior Ratio (LDIR) for describing the shape between a feature point and every other boundary points. A pair of points such that the LDIR are similar is called a correspondence. By using the correspondence points, the time complexity for computing the Modified Hausdorff Distance is improved. Moreover, the quality of the retrieved result is as good as applying the naive method.To deal with the large size of image database, it is important for the system to be able to locate and extract the shape contour of the important objects in an image automatically. We proposed a semi-automatic image segmentation algorithm. We employ an algorithm called a room-edge region for removing background region. In order to segment an image containing multiple objects, it can be segmented by decomposing the given pixel grid into small subgrids and apply the room-edge region for each subgrids. One limitation is'the quality of the segmented result depends on decomposition of the subgrids.We present two algorithms for decomposing an image optimally. The first one is called a quadtree decomposition, which an image is optimally decomposed using the quadtree structure. The second on is called an optimal baseline location, which optimally placed a partition lines.In the future, we plan to apply machine learning methods and other features such as color for improving the quality of the retrieved result. Moreover, the label attached to the image is taken into consideration in order to widening the scope of the search. Less

本研究的目的是提高图像检索的质量在现实世界中的应用一样高成功的文本检索方法。我们专注于应用的主要对象，这是从查询图像中提取的形状特征，用于识别图像之间的相似性。提出了两种算法。第一个是为了提高应用形状相似性度量时的时间复杂度。第二种算法是为了提高使用基本单调区域时图像分割的质量。同时为了让系统能够自动定位重要的物件，本研究使用一种称为修正Hausdorff距离的形状相似性度量。给定两组边界点P和Q，要使用修改的豪斯多夫距离比较两个形状，一个形状需要与另一个形状对齐。修改的Hausdorff距离是两个形状边界上的点之间最近点的平均距离。要获得最佳相似性， ...更多信息为了测量尺寸，形状必须对齐到彼此最相似的部分。在朴素方法中，所有的点对都被应用于寻找最佳变换。因此，时间复杂度是立方的边界点的大小。而不是应用所有可能的转换，我们提出了一种方法，应用一对对应点映射两个形状到彼此的相似部分。我们还提出了一个形状描述符称为局部距离内部比（LRDR）描述的形状之间的特征点和每一个其他的边界点。一对点使得两个直线相似称为对应。通过利用对应点，改进了计算修正Hausdorff距离的时间复杂度。为了处理大规模的图像数据库，系统必须能够自动定位和提取图像中重要目标的形状轮廓。提出了一种半自动的图像分割算法。我们采用一种称为房间边缘区域的算法来去除背景区域。为了分割包含多个对象的图像，可以通过将给定的像素网格分解成小的子网格并对每个子网格应用房间边缘区域来分割。本文提出了两种图像最优分解算法。第一种称为四叉树分解，它是使用四叉树结构对图像进行最佳分解。第二个称为最佳基线位置，它最佳地放置了分割线。未来，我们计划应用机器学习方法和其他功能，如颜色，以提高检索结果的质量。此外，为了扩大检索范围，还考虑了图像的标签。少

项目成果

期刊论文数量（0）

专著数量（0）

科研奖励数量（0）

会议论文数量（0）

专利数量（0）

Base-object location problems for base-monotone regions

DOI：
10.1016/j.tcs.2013.11.030
发表时间：
2014-10
期刊：
Theor. Comput. Sci.
影响因子：
0
作者：
Jinhee Chun;T. Horiyama;Takehiro Ito;Natsuda Kaothanthong;H. Ono;Y. Otachi;T. Tokuyama;Ryuhei Uehara;T. Uno
通讯作者：
Jinhee Chun;T. Horiyama;Takehiro Ito;Natsuda Kaothanthong;H. Ono;Y. Otachi;T. Tokuyama;Ryuhei Uehara;T. Uno

Algorithms for Computing Optimal Image Segmentation using Quadtree Decomposition

使用四叉树分解计算最佳图像分割的算法