【摘要】 图像联合分割是计算机视觉领域的一个研究热点。有能力在分割过程中利用相似对象的信息是联合分割相比于其他分割算法的一个优势,与此同时,建立对应对象的相似性也成为了一项具有挑战性的任务。文中为图像联合分割算法提出一个新的中心一致性敏感直方图。传统的图像直方图通过为每个出现在图像中的像素在该像素的对应灰度级计数器上加一的方式计算得出。与传统的图像直方图不同,中心敏感直方图在每个像素处计算,并且它是为每个出现的像素在其对应的灰度级计数器上加一个浮点数,这个浮点数服从对应像素与计算该直方图位置处的像素之间的空间一致性。因此,该直方图不仅从灰度级角度考虑了像素的分布,而且也将像素的空间相对位置信息考虑在内。基于该中心一致性敏感直方图,文中提出了一种强健的图像联合分割算法,其强健性主要体现在的对处于不同光照条件下和形状发生变化的相似对象进行较好的分割。基于大量的测试数据集对所提出的算法进行验证,实验结果表明,所提方法的分割正确率相比现有技术的平均水平提高了3个百分点左右,尤其当测试数据集中各个前景对象处于不同光照条件下或具有不同形状时效果更佳。更多还原

【Abstract】 Image co-segmentation is one of the active research areas in computer vision.The ability to utilize the information of similar objects in segmentation process is one of the advantages of co-segmentation,which is different from other segmentation methods.Meanwhile,establishing the similarity of corresponding objects is becoming a challenging task.This paper presented a novel consistency of center sensitive histogram for image co-segmentation.Unlike the traditional image histogram that calculates the frequency of occurrence for the intensity value by adding ones to the corresponding bin,a consistency of center sensitive histogram is computed at each pixel and a floating-point value is added to the corresponding bin for each occurrence of the intensity value.The floating-point value is a spatial consistency between the pixel of occurrence of intensity and the pixel where the histogram is computed.Therefore,the histogram not only takes the distribution of each pixel’s intensity value into account,but also the spatial relative position.A robust co-segmentation framework was proposed.Its robustness reflects the similar objects under different illumination and deformation condition can be both segmented well.The proposed technique was verified on various test image data sets.The experimental results demonstrate that the proposed method outperforms the average of state-of-the-art 3%,especially when the test image is in different illumination conditions and has different shapes.更多还原