As a newly emerging and significant topic in computer vision community, co-saliency detection aims at discovering the common salient objects in multiple related images. The existing methods often generate the co-saliency map through a direct forward pipeline which is based on the designed cues or initialization, but lack the refinement-cycle scheme. Moreover, they mainly focus on RGB image and ignore the depth information for RGBD images. In this paper, we propose an iterative RGBD co-saliency framework, which utilizes the existing single saliency maps as the initialization, and generates the final RGBD cosaliency map by using a refinement-cycle model. Three schemes are employed in the proposed RGBD co-saliency framework, which include the addition scheme, deletion scheme, and iteration scheme. The addition scheme is used to highlight the salient regions based on intra-image depth propagation and saliency propagation, while the deletion scheme filters the saliency regions and removes the non-common salient regions based on interimage constraint. The iteration scheme is proposed to obtain more homogeneous and consistent co-saliency map. Furthermore, a novel descriptor, named depth shape prior, is proposed in the addition scheme to introduce the depth information to enhance identification of co-salient objects. The proposed method can effectively exploit any existing 2D saliency model to work well in RGBD co-saliency scenarios. The experiments on two RGBD cosaliency datasets demonstrate the effectiveness of our proposed framework.
翻译:作为计算机视觉界中新出现的重要专题,共同认知探测的目的是在多个相关图像中发现共同突出对象。现有方法往往通过基于设计提示或初始化的直接前方管道生成共识别图,但缺乏精细周期计划。此外,它们主要侧重于RGB图像,忽视了RGBD图像的深度信息。在本文件中,我们提议了一个迭接的 RGBD共同识别框架,将现有的单一突出地图用作初始化,并通过使用精细周期模型生成最终RGBD共识别图。在拟议的RGBD共同识别框架框架中,采用了三种办法,其中包括添加计划、删除计划和迭代计划。添加计划主要侧重于基于图像深度传播和突出传播的突出区域,而删除计划过滤了突出区域,并删除了基于临时框架限制的非显著区域。提议采用该办法是为了在拟议中的RGBG-透明周期模型中获取更加统一和一致的共识别图。在拟议的RGB2共同定位框架中,在拟议的深度图中引入了三种办法,在拟议的前方定义的深度中引入了一种新的共同识别方法。