摘要
本文提出了一种基于分层复杂性度量的形状描述方法 (简称HCMD),以应用于一般的形状图像检索任务.该方法属于基于区域的形状描述方法,不需要专门提取形状的边界完成特征抽取任务.本文的主要贡献是提出了一种分层的形状描述框架,基于该框架,形状沿各个方向被迭代地切割成小的区块,每一层级的形状区块被施以各种几何特性的度量,以刻画其形状复杂性.这种分层抽取形状复杂性特征的描述机制使得HCMD具有由粗到细的形状描述能力,能有效地表达形状内部的复杂结构特性.基于HCMD的形状匹配,独立于形状的旋转、缩放、平移和镜像变换,而且计算简单,是一种能有效处理轮廓线形状和区域形状识别的通用方法.用MPEG-7的CE-1轮廓线形状图像库、CE-2区域形状图像库和美国哥伦比亚图像库COIL-20这3个标准测试集对HCMD进行性能评估,并与其他形状描述方法进行了广泛的比较,包括5种基于区域的形状描述子、4种基于点集的形状描述子和两种基于曲线的描述子.实验结果表明HCMD方法在综合考虑检索率、计算效率和一般应用能力指标下,其性能要优于参与比较的各类方法,证明了该方法的有效性.
A novel descriptor, called HCMD, which is based on hierarchical complexity measures, is proposed for generic shape based image retrieval. HCMD belongs to region-based methods and iteratively partitions a shape into smaller blocks along various directions. The geometrical properties of these smaller blocks, which are derived from each iterative cut, are measured to form a hierarchical description for the shape. The descriptor has the ability to characterize a shape from coarse to fine, and can effectively capture its complex inner structural features.Shape matching based on HCMD is independent of the rotation, scaling, translation, and mirror transform of the shape. It has low computational complexity and can effectively handle both the contour and region shapes. Three standard test sets, namely, the MPEG-7 CE-1 contour shape database, MPEG-7 CE-2 region shape database,and COIL-20 database, are used to evaluate the performance of the proposed HCMD, and extensive comparisons with state-of-the-art approaches, including five region-based descriptors, four point-set based descriptors, and two curve-based descriptors, are conducted. All experimental results indicate that the proposed HCMD outperforms these approaches in terms of their comprehensive performance based on the retrieval rates, retrieval efficiency,and general applications.
出处
《中国科学：信息科学》
CSCD
北大核心
2017年第12期1674-1693,共20页
Scientia Sinica(Informationis)
基金
国家自然科学基金(批准号:61372158)
江苏省自然科学基金(批准号:BK20141487)
江苏省"333工程"高层次人才工程(批准号:BRA2015351)
江苏省科技计划(产学研合作前瞻性联合研究)(批准号:BY2016009-03)
江苏高校优势学科建设工程(PAPD)资助项目
关键词
形状描述
分层复杂性度量
特征抽取
目标识别
图像检索
shape description, hierarchical complexity measures, feature extraction, object recognition, image retrieval
