# Exploiting Color Name Space for Salient Object Detection

Source: [Exploiting Color Name Space for Salient Object Detection](<https://www.sota2.com/research/paper/68fa66ce1e209131cea127c2>)

Collection index: [llms.txt](<https://www.sota2.com/research/llms.txt>)

Data updated: 2025-10-25T21:59:21.344Z

Authors: Jing Lou, Huan Wang, Longtao Chen, Fenglei Xu, Qingyuan Xia, Wei Zhu, Mingwu Ren

Published: 2017-03-27T03:08:58.000Z

## Abstract

In this paper, we will investigate the contribution of color names for the task of salient object detection. An input image is first converted to color name space, which is consisted of 11 probabilistic channels. By exploiting a surroundedness cue, we obtain a saliency map through a linear combination of a set of sequential attention maps. To overcome the limitation of only using the surroundedness cue, two global cues with respect to color names are invoked to guide the computation of a weighted saliency map. Finally, we integrate the above two saliency maps into a unified framework to generate the final result. In addition, an improved post-processing procedure is introduced to effectively suppress image backgrounds while uniformly highlight salient objects. Experimental results show that the proposed model produces more accurate saliency maps and performs well against twenty-one saliency models in terms of three evaluation metrics on three public data sets.

## Recorded benchmark results

A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage.

## Original sources and implementation

[Original paper](<https://arxiv.org/abs/1703.08912>)

[PDF](<https://arxiv.org/pdf/1703.08912.pdf>)