Review of Visual Saliency Detection with Comprehensive Information

TL;DR

This paper reviews various visual saliency detection methods, emphasizing the integration of comprehensive information like depth, inter-image correspondence, and motion, and discusses their challenges, datasets, and future directions.

Contribution

It provides a comprehensive overview of different saliency detection algorithms, summarizing key issues, evaluation datasets, and experimental analyses, highlighting future research directions.

Findings

RGBD saliency detection effectively combines depth cues.

Co-saliency detection discovers common salient objects across images.

Video saliency detection incorporates motion and spatiotemporal cues.

Abstract

Visual saliency detection model simulates the human visual system to perceive the scene, and has been widely used in many vision tasks. With the acquisition technology development, more comprehensive information, such as depth cue, inter-image correspondence, or temporal relationship, is available to extend image saliency detection to RGBD saliency detection, co-saliency detection, or video saliency detection. RGBD saliency detection model focuses on extracting the salient regions from RGBD images by combining the depth information. Co-saliency detection model introduces the inter-image correspondence constraint to discover the common salient object in an image group. The goal of video saliency detection model is to locate the motion-related salient object in video sequences, which considers the motion cue and spatiotemporal constraint jointly. In this paper, we review different types of saliency detection algorithms, summarize the important issues of the existing methods, and discuss the existent problems and future works. Moreover, the evaluation datasets and quantitative measurements are briefly introduced, and the experimental analysis and discission are conducted to provide a holistic overview of different saliency detection methods.