Unsupervised Saliency Detection Based on 2D Gabor and Curvelets Transforms

Sheng-hua Zhong, Yan Liu, Ling Shao, Gangshan Wu

Research output: Chapter in Book/Report/Conference proceedingConference contribution

2 Citations (Scopus)

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 Gabor transform'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.
Original languageEnglish
Title of host publicationICIMCS '11: Proceedings of the Third International Conference on Internet Multimedia Computing and Service
Place of PublicationNew York, NY, United States
PublisherACM
Pages146–149
ISBN (Print)9781450309189
DOIs
Publication statusPublished - 5 Aug 2011
EventICIMCS '11 - The Third International Conference on Internet Multimedia Computing and Service - Chengdu, China
Duration: 1 Aug 2011 → …

Conference

ConferenceICIMCS '11 - The Third International Conference on Internet Multimedia Computing and Service
Period1/08/11 → …

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