Adaptive integration of depth and color for objectness estimation

The goal of objectness estimation is to predict a moderate number of proposals of all possible objects in a given image with high efficiency. Most existing works solve this problem solely in conventional 2D color images. In this paper, we demonstrate that the depth information could benefit the estimation as a complementary cue to color information. After detailed analysis of depth characteristics, we present an adaptively integrated description for generic objects, which could take full advantages of both depth and color. With the proposed objectness description, the ambiguous area, especially the highly textured regions in original color maps, can be effectively discriminated. Meanwhile, the object boundary areas could be further emphasized, which leads to a more powerful objectness description. To evaluate the performance of the proposed approach, we conduct the experiments on two challenging datasets. The experimental results show that our proposed objectness description is more powerful and effective than state-of-the-art alternatives.