Saliency Detection combining Multi-layer Integration algorithm with background prior and energy function

TL;DR

This paper introduces a multi-layer saliency detection method that combines background prior, objectness labels, and an energy function to improve accuracy, validated by experiments on multiple datasets.

Contribution

The paper presents a novel multi-layer integration algorithm for saliency detection that combines background prior, foreground objectness, and energy optimization for enhanced performance.

Findings

Outperforms state-of-the-art algorithms on three datasets.

Effectively addresses scale variation in saliency maps.

Combines multiple priors for more accurate saliency detection.

Abstract

In this paper, we propose an improved mechanism for saliency detection. Firstly,based on a neoteric background prior selecting four corners of an image as background,we use color and spatial contrast with each superpixel to obtain a salinecy map(CBP). Inspired by reverse-measurement methods to improve the accuracy of measurement in Engineering,we employ the Objectness labels as foreground prior based on part of information of CBP to construct a map(OFP).Further,an original energy function is applied to optimize both of them respectively and a single-layer saliency map(SLP)is formed by merging the above twos.Finally,to deal with the scale problem,we obtain our multi-layer map(MLP) by presenting an integration algorithm to take advantage of multiple saliency maps. Quantitative and qualitative experiments on three datasets demonstrate that our method performs favorably against the state-of-the-art algorithm.