Consistent Video Saliency Using Local Gradient Flow Optimization and Global Refinement

Wenguan Wang, , Jianbing Shen, 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

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Abstract

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
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Faculty \ School: Faculty of Science > School of Computing Sciences
Depositing User: Pure Connector
Date Deposited: 16 Feb 2017 02:21
Last Modified: 25 Nov 2020 00:39
URI: https://ueaeprints.uea.ac.uk/id/eprint/62619
DOI: 10.1109/TIP.2015.2460013

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