CN103578123B - A kind of image-region merges method - Google Patents

A kind of image-region merges method Download PDF

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CN103578123B
CN103578123B CN201310469596.5A CN201310469596A CN103578123B CN 103578123 B CN103578123 B CN 103578123B CN 201310469596 A CN201310469596 A CN 201310469596A CN 103578123 B CN103578123 B CN 103578123B
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郑丽颖
田凯
石大明
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Harbin Engineering University
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Abstract

本发明涉及一种图像区域合并方法,其特征在于:图像区域A和图像区域B的颜色直方图距离通过如下方法获得,其中,式中,p(k)为图像区域A的颜色直方图,q(k)为图像区域B的颜色直方图,其中k=0,1,…,Ncolor-1,Ncolor表示彩色直方图所采用的颜色量化区域的个数,[A0,A1]为p(k)的支撑区域,[B0,B1]为q(k)的支撑区域,D(p,q)表示两图像区域颜色直方图之间的距离;根据颜色直方图距离D(p,q)的大小,判断是否对图像区域A和图像区域B进行合并。The present invention relates to a method for merging image areas, characterized in that: the color histogram distance between image area A and image area B is obtained by the following method, in, In the formula, p(k) is the color histogram of image area A, q(k) is the color histogram of image area B, where k=0,1,...,N color -1, N color represents the color histogram The number of color quantization regions used, [A 0 , A 1 ] is the support region of p(k), [B 0 , B 1 ] is the support region of q(k), and D(p,q) represents two images The distance between the region color histograms; according to the size of the color histogram distance D(p,q), it is judged whether to merge the image region A and the image region B.

Description

一种图像区域合并方法A method of image region merging

技术领域technical field

本发明涉及一种图像区域合并方法。The invention relates to a method for merging image regions.

背景技术Background technique

图像分割在模式识别和图像处理中扮演着重要角色。图像分割的结果直接决定了目标跟踪、图像理解和机遇目标的图像压缩技术的性能。目前,已提出了许多图像处理方法,例如:阈值分割法、特征空间聚类技术、基于边界的分割方法、基于区域的分割方法、图分割方法等等。近年来,图分割方法越来越受到研究者的重视。图分割方法主要研究的是节点聚类问题,主流方法包括正规化割(NormalizedCut,N-Cut)和比率割(RatioCut,R-Cut)。但是图分割方法有两个缺点:所需存贮空间大、计算速度慢。为了克服这两个缺点,研究者提出了一种多级图像分割方法,将基于区域的图像分割方法与图分割方法相结合。例如:Yang等人提出了基于分水岭方与图论的图像分割方法;Tao等人提出了基于均值移动(MeanShift,MS)与N-Cut的图像分割方法。Image segmentation plays an important role in pattern recognition and image processing. The results of image segmentation directly determine the performance of image compression techniques for object tracking, image understanding, and objects of opportunity. At present, many image processing methods have been proposed, such as: threshold segmentation method, feature space clustering technology, boundary-based segmentation method, region-based segmentation method, graph segmentation method and so on. In recent years, graph segmentation methods have attracted more and more attention from researchers. The graph segmentation method mainly studies the node clustering problem, and the mainstream methods include Normalized Cut (N-Cut) and Ratio Cut (RatioCut, R-Cut). However, the graph segmentation method has two disadvantages: the required storage space is large, and the calculation speed is slow. To overcome these two shortcomings, researchers propose a multi-level image segmentation method that combines region-based image segmentation methods with graph segmentation methods. For example: Yang et al. proposed an image segmentation method based on watershed square and graph theory; Tao et al. proposed an image segmentation method based on mean shift (MeanShift, MS) and N-Cut.

虽然这些基于区域的图像分割方法与图分割的多级图像分割方法在克服存贮空间和计算速度上具有较大优势,但是他们通常使用简单的特征(例如颜色均值)来表征图像区域。由于在多级图像分割中,图像经初级分割后得到的图像区域通常较大,用简单的特征向量无法完整的表示图像区域的特征。而研究表明:彩色直方图能够表示图像色彩分布的全局和局部特征。因此,图像的彩色直方图可以表示出更多的特征。但是,适用于模式匹配以及分类的欧氏距离、直方图相交距离、或者Bhattacharyya距离并不适用于图像区域合并。Although these region-based image segmentation methods and multi-level image segmentation methods of graph segmentation have great advantages in overcoming storage space and computing speed, they usually use simple features (such as color mean) to characterize image regions. Because in multi-level image segmentation, the image area obtained after the primary segmentation of the image is usually relatively large, the features of the image area cannot be fully represented by simple feature vectors. The research shows that: the color histogram can represent the global and local characteristics of the image color distribution. Therefore, the color histogram of an image can represent more features. However, Euclidean distance, histogram intersection distance, or Bhattacharyya distance, which are suitable for pattern matching and classification, are not suitable for image region merging.

发明内容Contents of the invention

本发明目的在于提供一种图像区域合并方法,能够有效实现图像区域的合并。The purpose of the present invention is to provide a method for merging image regions, which can effectively realize the merging of image regions.

实现本发明的技术方案:Realize the technical scheme of the present invention:

一种图像区域合并方法,其特征在于:A method for merging image regions, characterized in that:

图像区域A和图像区域B的颜色直方图距离通过如下方法获得,The color histogram distance of image area A and image area B is obtained by the following method,

其中, in,

式中,p(k)为图像区域A的颜色直方图,q(k)为图像区域B的颜色直方图,其中k=0,1,…,Ncolor-1,Ncolor表示彩色直方图所采用的颜色量化区域的个数,[A0,A1]为p(k)的支撑区域,[B0,B1]为q(k)的支撑区域,D(p,q)表示两图像区域颜色直方图之间的距离;In the formula, p(k) is the color histogram of image area A, q(k) is the color histogram of image area B, where k=0,1,...,N color -1, N color represents the color histogram The number of color quantization regions used, [A 0 , A 1 ] is the support region of p(k), [B 0 , B 1 ] is the support region of q(k), and D(p,q) represents two images distance between region color histograms;

根据颜色直方图距离D(p,q)的大小,判断是否对图像区域A和图像区域B进行合并。According to the size of the color histogram distance D(p,q), whether to merge the image area A and the image area B is judged.

当颜色直方图距离D(p,q)<某一阈值时,可对图像区域A和图像区域B进行合并。When the color histogram distance D(p,q)<a certain threshold, the image area A and the image area B may be merged.

本发明具有的有益效果:The beneficial effect that the present invention has:

本发明所提出的颜色直方图距离计算方法,充分考虑了多级图像分割中图像区域的特点,图像区域合并的正确率高,并且方法简单易行。The color histogram distance calculation method proposed by the present invention fully considers the characteristics of image regions in multi-level image segmentation, has a high accuracy of image region merging, and is simple and easy to implement.

附图说明Description of drawings

图1是实施例图像;Fig. 1 is embodiment image;

图2是图1中A、B、C图像区域的颜色直方图。Fig. 2 is a color histogram of image regions A, B, and C in Fig. 1 .

具体实施方式detailed description

设A、B表示两个图像区域,p(k)为区域A的颜色直方图,q(k)为区域B的颜色直方图,其中k=0,1,…,Ncolor-1,Ncolor表示彩色直方图所采用的颜色量化区域的个数,[A0,A1]为p(k)的支撑区域,即:p(k)≠0若k∈[A0,A1],[B0,B1]为q(k)的支撑区域,即:q(k)≠0若k∈[B0,B1]。定义区域A、B的直方图距离为:Let A and B denote two image regions, p(k) is the color histogram of region A, q(k) is the color histogram of region B, where k=0,1,...,N color -1, N color Indicates the number of color quantization areas used in the color histogram, [A 0 ,A 1 ] is the support area of p(k), that is: p(k)≠0 if k∈[A 0 ,A 1 ], [ B 0 , B 1 ] is the support area of q(k), namely: q(k)≠0 if k∈[B 0 , B 1 ]. Define the histogram distance of regions A and B as:

其中, in,

公式(1)中,D(p,q)表示直方图p和q之间的距离,∩表示求集合的交集。In formula (1), D(p,q) represents the distance between the histograms p and q, and ∩ represents the intersection of sets.

公式(1)表明:1)若区域A包含所有在区域B中出现的颜色,则A、B之间的直方图距离为0;2)类似地,若区域B包含所有在区域A中出现的颜色,则A、B之间的直方图距离也为0;3)若区域A不包含区域B中的任何颜色,则A、B之间的直方图距离也为1;4)若不是上述1)、2)、3)中情况,则A、B之间的直方图距离为其中K0、K1的取值与两个集合相交的情况有关,由公式(2)确定。Formula (1) shows that: 1) If region A contains all colors that appear in region B, then the histogram distance between A and B is 0; 2) similarly, if region B contains all colors that appear in region A color, the histogram distance between A and B is also 0; 3) If the region A does not contain any color in region B, the histogram distance between A and B is also 1; 4) If it is not the above 1 ), 2), and 3), the histogram distance between A and B is The values of K 0 and K 1 are related to the intersection of the two sets, and are determined by formula (2).

下面结合具体实施例,进一步说明本发明的有益效果。The beneficial effects of the present invention will be further described below in conjunction with specific examples.

图1显示了一幅图像及其三个区域A、B和C,图2给出了区域A、B和C颜色直方图,其中Ncolor=512。利用公式(1)计算得到的区域A、B的直方图距离为0.4912,区域A、C的直方图距离为0.0795,区域B、C的直方图距离为0.9635。由此,我们可以判断出,区域A、C的颜色很接近,可以合并为一个区域。这与人眼的聚类结果相一致。Fig. 1 shows an image and its three regions A, B and C, and Fig. 2 shows the color histograms of regions A, B and C, where N color =512. The histogram distance between regions A and B calculated by formula (1) is 0.4912, the histogram distance between regions A and C is 0.0795, and the histogram distance between regions B and C is 0.9635. From this, we can judge that the colors of regions A and C are very close and can be merged into one region. This is consistent with the clustering results of the human eye.

另一方面,如果采用传统的Bhattacharya距离,则区域A、B的直方图距离为0.9995,区域A、C的直方图距离为0.6472,区域B、C的直方图距离为0.9993。区域A、C的直方图距离为0.6472,表明这两个区域的颜色不相似,不应合并为一个区域,这显然与人眼的分类结果不相符。On the other hand, if the traditional Bhattacharya distance is used, the histogram distance of the regions A, B is 0.9995, the histogram distance of the regions A, C is 0.6472, and the histogram distance of the regions B, C is 0.9993. The histogram distance of regions A and C is 0.6472, indicating that the colors of these two regions are not similar and should not be merged into one region, which obviously does not match the classification results of the human eye.

Claims (1)

1.一种图像区域合并方法,其特征在于:1. A method for merging image regions, characterized in that: 图像区域A和图像区域B的颜色直方图距离通过如下方法获得,The color histogram distance of image area A and image area B is obtained by the following method, 其中, in, 式中,p(k)为图像区域A的颜色直方图,q(k)为图像区域B的颜色直方图,其中k=0,1,…,Ncolor-1,Ncolor表示彩色直方图所采用的颜色量化区域的个数,[A0,A1]为p(k)的支撑区域,[B0,B1]为q(k)的支撑区域,D(p,q)表示两图像区域颜色直方图之间的距离:1)若区域A包含所有在区域B中出现的颜色,则A、B之间的直方图距离为0;2)若区域B包含所有在区域A中出现的颜色,则A、B之间的直方图距离也为0;3)若区域A不包含区域B中的任何颜色,则A、B之间的直方图距离为1;4)若不是上述1)、2)、3)中情况,则A、B之间的直方图距离为 In the formula, p(k) is the color histogram of image area A, q(k) is the color histogram of image area B, where k=0,1,...,N color -1, N color represents the color histogram The number of color quantization regions used, [A 0 , A 1 ] is the support region of p(k), [B 0 , B 1 ] is the support region of q(k), and D(p,q) represents two images Distance between region color histograms: 1) If region A contains all colors that appear in region B, then the histogram distance between A and B is 0; 2) If region B contains all colors that appear in region A color, the histogram distance between A and B is also 0; 3) If region A does not contain any color in region B, then the histogram distance between A and B is 1; 4) If not the above 1) , 2), and 3), the histogram distance between A and B is 根据颜色直方图距离D(p,q)的大小,判断是否对图像区域A和图像区域B进行合并。According to the size of the color histogram distance D(p,q), whether to merge the image area A and the image area B is judged.
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Citations (2)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN101625763A (en) * 2009-04-17 2010-01-13 华中科技大学 Method for measuring similarity of spatial color histogram
CN103065325A (en) * 2012-12-20 2013-04-24 中国科学院上海微系统与信息技术研究所 Target tracking method based on color distance of multicolors and image dividing and aggregating

Family Cites Families (1)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US6708167B2 (en) * 1999-11-29 2004-03-16 Lg Electronics, Inc. Method for searching multimedia data using color histogram

Patent Citations (2)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN101625763A (en) * 2009-04-17 2010-01-13 华中科技大学 Method for measuring similarity of spatial color histogram
CN103065325A (en) * 2012-12-20 2013-04-24 中国科学院上海微系统与信息技术研究所 Target tracking method based on color distance of multicolors and image dividing and aggregating

Non-Patent Citations (3)

* Cited by examiner, † Cited by third party
Title
Interactive image segmentation by maximal similarity based region merging;Jifeng Ning等;《Pattern Recognition》;20101231;第43卷;第445-456页 *
一种基于改进粒子滤波的多目标跟踪算法;刘国成等;《控制与决策》;20090228;第24卷(第2期);第317-320页 *
一种基于空间直方图的Mean-Shift跟踪算法;胡彬等;《计算机应用研究》;20100630;第27卷(第6期);第2394-2397页 *

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