Adaptive alpha-trimmed average operator based on Gaussian distribution hypothesis test for image representation

Cheng Cai, Kin Man Lam, Zheng Tan

Research output: Chapter in book / Conference proceedingConference article published in proceeding or bookAcademic researchpeer-review

Abstract

Representation operator is one of the key issues of the content-based retrieval. In this paper, we propose an adaptive alpha-trimmed average operator based on Gaussian distribution hypothesis test for image representation. The adaptive alpha-trimmed average operator extracts the representation by trimming outliers and then estimating the central value of the rest. Since the more samples are used, the more accurate representation we get, the optimal trimming parameter should guarantee to remove the extreme values and at the same time keep useful samples as more as possible. The criterion to distinguish between useful data and extreme noise is derived from Gaussian distribution hypothesis test on the basis of global statistics. Experimental results from standard images show that our proposed scheme outperforms traditional adaptive methods.
Original languageEnglish
Title of host publicationProceedings of the 4th International Conference on Image and Graphics, ICIG 2007
Pages810-814
Number of pages5
DOIs
Publication statusPublished - 1 Dec 2007
Event4th International Conference on Image and Graphics, ICIG 2007 - Chengdu, China
Duration: 22 Aug 200724 Aug 2007

Conference

Conference4th International Conference on Image and Graphics, ICIG 2007
CountryChina
CityChengdu
Period22/08/0724/08/07

ASJC Scopus subject areas

  • Computer Graphics and Computer-Aided Design

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