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Significant Perceptual Regions by Active-Nets

机译:Active-Nets的重要感知区域

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摘要

The available visual information is quickly growing now a days, it is the reason of the emerging of a new research field, oriented to the automatic retrieval of this kind of information. These systems usually uses perceptual features of the images (color, shape, texture, ... ). There is an important gap between the features used by the CBIR systems and the human perception of the information of an image. This work introduces a technique to extract significant perceptual regions of an image. The developed algorithm uses a bidimensional active model, active nets, these nets are guided by the chromatic components of a perceptual color space of the tested image. The restriction to only chromatic information made the fitting of an active net to the significant perceptual regions more tolerant to illumination problems of the image. The final objective will be to associate significant perceptual regions with semantic descriptors of the objects present in an image.
机译:如今,可用的视觉信息正在迅速增长,这是新兴的研究领域兴起的原因,该领域面向自动检索此类信息。这些系统通常使用图像的感知特征(颜色,形状,纹理等)。 CBIR系统使用的功能与人类对图像信息的感知之间存在重要的鸿沟。这项工作介绍了一种提取图像的重要感知区域的技术。所开发的算法使用二维主动模型,即主动网络,这些网络由被测图像的感知色彩空间的色度分量引导。仅对色度信息的限制使得有源网络对重要的感知区域的拟合更能容忍图像的照明问题。最终目标是将重要的感知区域与图像中存在的对象的语义描述符相关联。

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