基于轮廓重构和特征点弦长的图像检索

Image Retrieval Based on Contour Reconstruction and Feature Point Chord Length

轮廓描述法作为形状检索中最为关键的步骤,应体现目标的整体形状信息和重要特征点信息,并具备对噪声干扰的鲁棒性.提出一种基于轮廓重构和特征点弦长的图像检索算法,首先在目标轮廓提取的基础上分析轮廓的能量保持率,并进行轮廓的降维重构处理,从而减少了随机噪声造成的轮廓畸变.然后,通过新定义的支持域来计算轮廓点的特征强度,并分析了支持域半径与特征点提取结果的关系,从而筛选出有效的轮廓特征点.最后,根据轮廓点和相应特征点间的弦长关系构造轮廓特征函数,经相应处理后,最终得到的形状描述子满足不变性要求.大量实验结果表明,该算法无论是在常规样本库中,还是在噪声样本库中都具有更优的检索性能.

As the most important step in shape-based image retrieval, the description of image contour should reflect the information of global shape and key points, and be robust to random noise. This paper proposes a new image retrieval method based on contour reconstruction and feature point chord length. First, the contour of the shape is extracted, and in order to reduce the distortion caused by random noise, the contour is reconstructed by analyzing the energy retention rate. Then, base on the new defined supportive region, the feature intensity is calculated at each point of the contour to extract the valid feature points. After that, the contour feature function is structured by using the chord length between contour points and corresponding feature points. Finally, the shape descriptors are processed to meet the invariance property. A significant amount of experiments show that, in both normal and noisy sample sets, the proposed method demonstrates better performance compared with other seven techniques.