Consistent video saliency using local gradient flow optimization and global refinement

Wang, Wenguan, Shen, Jianbing and Shao, Ling (2015) Consistent video saliency using local gradient flow optimization and global refinement. IEEE Transactions on Image Processing, 24 (11). pp. 4185-4196. ISSN 1057-7149

[thumbnail of Accepted manuscript]
PDF (Accepted manuscript) - Accepted Version
Download (1MB) | Preview


We present a novel spatiotemporal saliency detection method to estimate salient regions in videos based on the gradient flow field and energy optimization. The proposed gradient flow field incorporates two distinctive features: 1) intra-frame boundary information and 2) inter-frame motion information together for indicating the salient regions. Based on the effective utilization of both intra-frame and inter-frame information in the gradient flow field, our algorithm is robust enough to estimate the object and background in complex scenes with various motion patterns and appearances. Then, we introduce local as well as global contrast saliency measures using the foreground and background information estimated from the gradient flow field. These enhanced contrast saliency cues uniformly highlight an entire object. We further propose a new energy function to encourage the spatiotemporal consistency of the output saliency maps, which is seldom explored in previous video saliency methods. The experimental results show that the proposed algorithm outperforms state-of-the-art video saliency detection methods.

Item Type: Article
Additional Information: (c) 2015 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other users, including reprinting/ republishing this material for advertising or promotional purposes, creating new collective works for resale or redistribution to servers or lists, or reuse of any copyrighted components of this work in other works.
Faculty \ School: Faculty of Science > School of Computing Sciences
Depositing User: Pure Connector
Date Deposited: 16 Feb 2017 02:21
Last Modified: 03 Jul 2023 10:30
DOI: 10.1109/TIP.2015.2460013


Downloads per month over past year

Actions (login required)

View Item View Item