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中文摘要：
显著性目标检测,在包括图像/视频分割、目标识别等在内的许多计算机视觉问题中是极为重要的一 步,有着十分广泛的应用前景.从显著性检测模型过去近1 0年的发展历程可以清楚看到,多数检测方法是采用 视觉特征来检测的,视觉特征决定了显著性检测模型的性能和效果.各类显著性检测模型的根本差异之一就是 所选用的视觉特征不同.首次较为全面地回顾和总结常用的颜色、纹理、背景等视觉特征,对它们进行了分类、比 较和分析.先从各种颜色特征中挑选较好的特征进行融合,然后将颜色特征与其他特征进行比较,并从中选择较 优的特征进行融合.在具有挑战性的公开数据集ESSCD、 DUT一0M0N上进行了实验,从 P R 曲线、 F一Measure方 法、M AE绝对误差三个方面进行了定量比较,检测出的综合效果优于其他算法.通过对不同视觉特征的比较和 融合,表明颜色、纹理、边框连接性、 Objectness这四种特征在显著性目标检测中是非常有效的.
英文摘要：
The saliency object detection is a very important step in many computer vision problems, including video image segmentation, target recognition, and has a very broad application prospect. Over the past 10 years of development of the apparent test model, it can be clearly seen that most of the detection methods are detected by using visual features, and the visual characteristics determine the performance and effectiveness of the significance test model. One of the fundamental differences between the various saliency detection models is the chosen of visual features. We reviewed and summarized the common visual features for the first time, such as color, texture and background. We classified them, compared and analyzed them. Firstly, we selected the better features from all kinds of color features to fuse, and then compared the color features with other characteristics, and chosen the best features to fuse. On the challenging open datasets ESSCD and DUT-OMON, the quantitative comparison was made from three aspects PR curve, F-measure method and MAE mean error, and the comprehensive effect was better than other algorithms. By comparing and merging different visual features, it is shown that the four characteristics of color, texture, border connectivity and Objectness are very effective in the saliency object detection.
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