Iterative Region Merging and Object Retrieval Method Using Mean Shift Segmentation and Flood Fill Algorithm

N. Bhargava, P. Trivedi, A. Toshniwal, H. Swarnkar
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引用次数: 11

Abstract

Nowadays image retrieval plays an important role in an extraordinary number of multimedia applications which serve human society. Since a generic graph based retrieval scheme working in every situation can be quite difficult to implement due to the presence of complex scene. In this paper we investigate to deal with such problems and focuses on how to extract the object from an image using topological models. It begins with the innovative concept of initial low level segmentation of input image, which are used to construct topological model on the basis of connected regions. Using this topological model we have design a new prototype known as Iteratively Region Merging and Object Retrieval (IRMOR). Using this prototype IRMOR we can extracted contour based descriptors on an object from an image which constituent the high level features. In addition we focus on how to apply a region labeling and flood fill method to extract object after formation of object contour, IRMOR have special application such an image matching, object recognition, content based image retrieval, object tracking etc.
基于均值偏移分割和洪水填充算法的迭代区域合并与目标检索方法
目前,在服务于人类社会的大量多媒体应用中,图像检索发挥着重要的作用。由于复杂场景的存在,通用的基于图的检索方案很难实现。在本文中,我们研究了如何处理这些问题,并重点研究了如何使用拓扑模型从图像中提取目标。首先提出了对输入图像进行初始低层次分割的创新概念,利用该概念在连通区域的基础上构建拓扑模型。利用这一拓扑模型,我们设计了一个新的原型,即迭代区域合并和对象检索(IRMOR)。利用该原型IRMOR,我们可以从图像中提取出基于轮廓的描述子,这些描述子构成了图像的高级特征。此外,我们还重点研究了如何在形成目标轮廓后应用区域标记和洪水填充方法提取目标,IRMOR在图像匹配、目标识别、基于内容的图像检索、目标跟踪等方面有特殊的应用。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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