Automatic model-based semantic object extraction algorithm
Automatic model-based semantic object extraction algorithm
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DOI：
10.1109/76.954494
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发表时间：
2001-10
期刊：
影响因子：
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通讯作者：
Jianping Fan;Xingquan Zhu;Lide Wu
中科院分区：
文献类型：
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作者：
Jianping Fan;Xingquan Zhu;Lide Wu
自动图像分割和对象提取在支持基于内容的图像编码、索引和检索方面起着重要作用。然而，用于分割的低级视觉同质性关键（如颜色、纹理、强度等）不会直接导致语义对象，因为语义对象可以包含完全不同的灰度、颜色或纹理。我们提出了一种基于模型的语义对象自动提取算法，该算法将对象种子与其区域约束图（感知模型）相结合。首先将基于相似度的区域生长和边缘检测结果结合，将图像分割成具有精确边界的均匀区域。我们提出了一种一维快速熵阈值技术来自动确定用于区域生长和边缘检测的阈值。对象种子是语义对象的直观和代表性部分，然后与这些同质图像区域区分开来。根据对象的感知模型，对检测到的对象种子的相邻区域进行合并，得到语义对象。我们将人脸作为对象种子，并使用基于比例的感知模型，专注于语义人类对象的生成。
Automatic image segmentation and object extraction play an important role in supporting content-based image coding, indexing, and retrieval. However, the low-level visual homogeneity critical (like color, texture, intensity, and so on) for segmentation do not lead to semantic objects directly because a semantic object can contain totally different gray levels, color, or texture. We propose an automatic model-based semantic object extraction algorithm by integrating object seeds with their region constraint graphs (perceptual models). Images are first partitioned into a set of homogeneous regions with accurate boundaries by integrating the results obtained by similarity-based region growing and edge detection procedures. We propose a 1-D fast entropic thresholding technique for determining the thresholds used in region growing and edge detection automatically. The object seeds, which are the intuitive and representative parts of semantic objects, are then distinguished from these homogeneous image regions. A seeded region aggregation procedure is used for merging the adjacent regions of a detected object seed to give a semantic object according to the perceptual model of the object. We focus on semantic human object generation by taking faces as object seeds and using a ratio-based perceptual model.