Journal of Computer Applications ›› 2017, Vol. 37 ›› Issue (6): 1753-1758.DOI: 10.11772/j.issn.1001-9081.2017.06.1753

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Sketch-based image retrieval method using local geometry moment invariant

BAO Zhenhua1, KANG Baosheng1, ZHANG Lei1,2, ZHANG Jing1   

  1. 1. College of Information Science and Technology, Northwest University, Xi'an Shaanxi 710127, China;
    2. College of Public Computer Teaching, Yuncheng University, Yuncheng Shanxi 044000, China
  • Received:2016-12-09 Revised:2017-02-16 Online:2017-06-10 Published:2017-06-14
  • Supported by:
    This work is partially supported by the National Natural Science Foundation of China (61272286), the Natural Science Basic Research Plan in Shaanxi Province (2014JM8346).


鲍振华1, 康宝生1, 张雷1,2, 张婧1   

  1. 1. 西北大学 信息科学与技术学院, 西安 710127;
    2. 运城学院 公共计算机教学部, 山西 运城 044000
  • 通讯作者: 康宝生
  • 作者简介:鲍振华(1992-),男,山西运城人,硕士研究生,主要研究方向:图像识别和检索;康宝生(1961-),男,陕西咸阳人,教授,博士,主要研究方向:图形图像处理;张雷(1980-)男,山西临猗人,讲师,博士,主要研究方向:图形图像处理;张婧(1988-)女,陕西西安人,博士研究生,主要研究方向:三维模型检索。
  • 基金资助:

Abstract: The difficulty in sketch-based image retrieval is the effective recognition of images with different scales, positions, rotations and deformations. In order to identify and retrieve images of different scales, positions and rotations more accurately, a Sketch-Based Image Retrieval method Using Local Geometry Moment Invariant (SBIRULGMI) was proposed. Firstly, the geometric characteristics of image were used to determine the coordinate system of image. Secondly, the geometry moment invariant of image blocks which were divided averagely based on the generated coordinate system was calculated to form a eigenvector. Then, the similarities between query sketch and images in database were calculated based on Euclidean distance. Finally, the retrieval results were obtained from the similarity ranking and optimized according to Ant Colony Optimization (ACO). Compared with Shape Context (SC), Edge Orientation Histogram (EOH), GAbor Local lIne-based Feature (GALIF) and MindFinder, the retrieval accuracy of the proposed method in image database of MPEG-7 shape1 part B was increased by 17 percentage points on average. The experimental results show that the proposed method not only has a better recognition effect on the images after translation, scaling and flipping transformation, but also has better robustness to a certain degree of rotation and deformation.

Key words: image block, geometry moment invariant, sketch, retrieval, Ant Colony Optimization (ACO) algorithm

摘要: 利用草图进行图像检索的难点在于对不同尺度、位置、旋转及形变图像的有效检索。为了更准确地识别并检索不同尺度、位置和旋转的图像,提出一种基于草图局部几何不变矩的图像检索方法(SBIRULGMI)。首先,利用图像的几何特征分别确定各图像的坐标系;然后,在生成的坐标系中对图像进行平均分块并计算各块的几何不变矩作为特征向量;接着,用改进的欧氏距离计算目标图像与数据库图像的相似度;最后,采用蚁群(ACO)算法对按照相似度排序后的检索结果进行优化。所提方法在MPEG-7 shape1 part B图像数据库的检索识别准确率比形状上下文(SC)、边缘分布直方图(EOH)、局部线性高波特征(GALIF)及MindFinder方法平均提高了17个百分点。实验结果表明该方法对不同平移、缩放和翻转的图像有较好的识别效果,对图像一定程度的旋转和形变具有更好的鲁棒性。

关键词: 图像分块, 几何不变矩, 草图, 检索, 蚁群算法

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