Unsupervised saliency detection based on 2D Gabor and Curvelets transforms

Authors:
Sheng-hua Zhong;Yan Liu;Ling Shao;Gangshan Wu
Affiliations:
The Hong Kong Polytechnic University, Hong Kong, P. R. China;The Hong Kong Polytechnic University, Hong Kong, P. R. China;University of Sheffield, Sheffield, United Kingdom;Nanjing University, P. R. China
Venue:
Proceedings of the Third International Conference on Internet Multimedia Computing and Service
Year:
2011

Citing 5
Cited 0

A Model of Saliency-Based Visual Attention for Rapid Scene Analysis

IEEE Transactions on Pattern Analysis and Machine Intelligence
Seam carving for content-aware image resizing

ACM SIGGRAPH 2007 papers
Saliency-based video segmentation with graph cuts and sequentially updated priors

ICME'09 Proceedings of the 2009 IEEE international conference on Multimedia and Expo
Automatic interesting object extraction from images using complementary saliency maps

Proceedings of the international conference on Multimedia
Saliency detection based on 2D log-gabor wavelets and center bias

Proceedings of the international conference on Multimedia

Quantified Score

Hi-index	0.00

Visualization

Abstract

Construction of saliency map in multimedia data is useful for applications in multimedia like object segmentation, quality assessment, and object recognition. In this paper, we propose a novel saliency map model called Gabor & Curvelets based Saliency Map (GCSMP) relying on 2D Gabor and Curvelet transforms. Compared with the traditional model based on DOG and wavelets, our model takes advantage of Garbor transforms's spatial localization and Curvelet transform's edge and directional information. We also discuss the influence of center bias and object detectors in our model. Empirical validations on standard dataset demonstrate the effectiveness of the proposed technique.