Guided Interactive Video Object Segmentation Using Reliability-Based Attention Maps
Yuk Heo, Yeong Jun Koh, Chang‐Su Kim
Abstract
We propose a novel guided interactive segmentation (GIS) algorithm for video objects to improve the segmentation accuracy and reduce the interaction time. First, we design the reliability-based attention module to analyze the reliability of multiple annotated frames. Second, we develop the intersection-aware propagation module to propagate segmentation results to neighboring frames. Third, we introduce the GIS mechanism for a user to select unsatisfactory frames quickly with less effort. Experimental results demonstrate that the proposed algorithm provides more accurate segmentation results at a faster speed than conventional algorithms. Codes are available at https://github.com/yuk6heo/GIS-RAmap.
Topics & Concepts
Computer scienceSegmentationReliability (semiconductor)Intersection (aeronautics)Frame (networking)Image segmentationObject (grammar)Artificial intelligenceScale-space segmentationComputer visionSegmentation-based object categorizationData miningQuantum mechanicsTelecommunicationsEngineeringAerospace engineeringPower (physics)PhysicsVisual Attention and Saliency DetectionAdvanced Image and Video Retrieval TechniquesVideo Surveillance and Tracking Methods