Unsupervised Detection for Minimizing a Region of Interest around Distinct Object in Natural Images

Anucha Tungkatsathan, W. Premchaiswadi, Nucharee Premchaiswadi
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引用次数: 1

Abstract

One of the major challenges for region-based image retrieval is to identify the Region of Interest (ROI) that comprises object queries. However, automatically identifying the regions or objects of interest in a natural scene is a very difficult task because the content is complex and can be any shape. In this paper, we present a novel unsupervised detection method to automatically and efficiently minimize the ROI in the images. We applied an edge-based active contour model that drew upon edge information in local regions. The mathematical implementation of the proposed active contour model was accomplished using a variational level set formulation. In addition, the mean-shift algorithm was used to reduce the sensitivity of parameter change of level set formulation. The results show that our method can overcome the difficulties of non-uniform sub-region and intensity in homogeneities in natural image segmentation.
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自然图像中不同目标周围兴趣区域最小化的无监督检测
基于区域的图像检索面临的主要挑战之一是识别包含对象查询的感兴趣区域(ROI)。然而,在自然场景中自动识别感兴趣的区域或物体是一项非常困难的任务,因为内容很复杂,可以是任何形状。在本文中,我们提出了一种新的无监督检测方法来自动有效地最小化图像中的ROI。我们应用了一种基于边缘的活动轮廓模型,该模型利用了局部区域的边缘信息。利用变分水平集公式实现了所提出的活动轮廓模型的数学实现。此外,采用mean-shift算法降低了水平集公式参数变化的敏感性。结果表明,该方法克服了自然图像分割中均匀性中子区域和强度不均匀的困难。
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