# Salient Object Detection via Objectness Measure

arXiv: https://arxiv.org/abs/1506.07363 · HTML: https://mlsanity.com/paper/1506.07363/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 24 Jun 2015.

## Built on by (most cited first, 6 of 6)

- [BiconNet: An Edge-preserved Connectivity-based Approach for Salient Object Detection](https://mlsanity.com/paper/2103.00334/) (2021)
- [GlassNet: Label Decoupling-based Three-stream Neural Network for Robust Image Glass Detection](https://mlsanity.com/paper/2108.11117/) (2021)
- [Lightweight high-resolution Subject Matting in the Real World](https://mlsanity.com/paper/2312.07100/) (2023)
- [Spatial Coherence Loss: All Objects Matter in Salient and Camouflaged Object Detection](https://mlsanity.com/paper/2402.18698/) (2024)
- [Semi-Supervised Cross-Modal Salient Object Detection with U-Structure Networks](https://mlsanity.com/paper/2208.04361/) (2022)
- [A new method for optical steel rope non-destructive damage detection](https://mlsanity.com/paper/2402.03843/) (2024)

## Abstract (arXiv)

Salient object detection has become an important task in many image processing applications. The existing approaches exploit background prior and contrast prior to attain state of the art results. In this paper, instead of using background cues, we estimate the foreground regions in an image using objectness proposals and utilize it to obtain smooth and accurate saliency maps. We propose a novel saliency measure called `foreground connectivity' which determines how tightly a pixel or a region is connected to the estimated foreground. We use the values assigned by this measure as foreground weights and integrate these in an optimization framework to obtain the final saliency maps. We extensively evaluate the proposed approach on two benchmark databases and demonstrate that the results obtained are better than the existing state of the art approaches.