Spatial Keypoint Representation for Visual Object Retrieval
Spatial Keypoint Representation for Visual Object Retrieval
复制标题
DOI：
10.1007/978-3-319-07176-3_56
复制
发表时间：
2014-06
期刊：
影响因子：
--
通讯作者：
Tomasz Nowak;Patryk Najgebauer;J. Romanowski;Marcin Gabryel;M. Korytkowski;R. Scherer;Dimche Kostadinov
中科院分区：
文献类型：
--
作者：
Tomasz Nowak;Patryk Najgebauer;J. Romanowski;Marcin Gabryel;M. Korytkowski;R. Scherer;Dimche Kostadinov
本文提出了一种基于 SURF 算法生成的图像关键点的对象预分类方法的概念。为此，该方法使用关键点直方图进行图像序列化，并使用下一个直方图树表示来加速比较过程。所提出的方法根据生成的关键点的定位为每个图像生成直方图。每个直方图包含 72 个根据关键点计算得出的值，这些关键点对应于对整个图像进行切片的扇区。扇区从作为分类对象的对象的中心点沿径向划分图像。生成的直方图允许存储对象形状的信息，并且还允许通过确定直方图之间的偏差来有效地比较形状。此外，从一组图像直方图生成的树结构可以进一步加快图像比较的过程。在这种方法中，每个直方图都作为分支添加到树中。子树以相反的顺序创建。最低级别的最后一个元素存储整个直方图。每个下一个上层元素都是其子元素的简化版本。这种方法允许按父节点对直方图进行分组，并减少节点比较的数量。如果元素不匹配，则省略其整个子树。最终结果是一组相似的图像，可以通过更复杂的方法进行处理。
This paper presents a concept of an object pre-classification method based on image keypoints generated by the SURF algorithm. For this purpose, the method uses keypoints histograms for image serialization and next histograms tree representation to speed-up the comparison process. Presented method generates histograms for each image based on localization of generated keypoints. Each histogram contains 72 values computed from keypoints that correspond to sectors that slice the entire image. Sectors divide image in radial direction form center points of objects that are the subject of classification. Generated histograms allow to store information of the object shape and also allow to compare shapes efficiently by determining the deviation between histograms. Moreover, a tree structure generated from a set of image histograms allows to further speed up process of image comparison. In this approach each histogram is added to a tree as a branch. The sub tree is created in a reverse order. The last element of the lowest level stores the entire histogram. Each next upper element is a simplified version of its child. This approach allows to group histograms by their parent node and reduce the number of node comparisons. In case of not matched element, its entire subtree is omitted. The final result is a set of similar images that could be processed by more complex methods.