基于水流方法和图像边界对比图的显著目标检测

Mangalraj Mangalraj, Akankshya Mohanty, Sakshi Singh
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引用次数: 0

摘要

图像或视频中的突出对象是对人类具有吸引力的对象。一个突出的物体与其背景相比具有更突出的特征。与系统相比,对一个突出对象的解释对人类来说是一项更容易的任务。因此,我们需要不同的显著目标检测算法,这些算法被输入到计算机中以从图像或视频中分割出显著目标。在目标检测、目标分类、图像压缩等后处理应用中,检测显著目标是一项至关重要的任务。全局特征表明,通过识别覆盖较大区域的主要部分的元素来获得显著目标。此外,由于辐射强度被表示为局部特征,因此显著目标与背景的区分边界不可分割。在研究工作中,提出了一种基于最小屏障距离和图像边界对比图的水流驱动显著目标检测方法。实验结果和分析表明,Precision-Recall、Mean Absolute和F-measure与现有的方法相比有了显著的改进。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Salient Object Detection Using Water Flow Approach and Image Boundary Contrast Map
A salient object in an image or video is the object which looks attractive to the human beings. A salient object has more prominent feature as compared to its background. Interpretation of a salient object is an easier task for human being as compared to systems. Thus, we need different salient object detection algorithms, which are feed into the computer to segment the salient object from the image or video. Detecting a salient object is a crucial task for post processing applications such as object detection, object classification, image compression and so on. Global feature elucidates that salient object is obtained by identifying the element covering major part of larger area. Furthermore, the differentiating boundary between the salient object and background is inseparable due to radiometric intensity which is represented as a local feature. In the research work, a novel salient object detection method has been proposed as Water Flow Driven using Minimum Barrier Distance and Image Boundary Contrast Map. The experimental results and analysis state that Precision-Recall, Mean Absolute and the F-measure has been significantly compared and improved with the existing work.
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