Feature guide: A statistically based feature selection scheme

Jia You; T. Dillon; E. Pissaloux

Feature guide: A statistically based feature selection scheme

Jia You, T. Dillon, E. Pissaloux

Research output: Chapter in book / Conference proceeding › Conference article published in proceeding or book › Academic research › peer-review

1 Citation (Scopus)

Abstract

This paper presents a new approach to content-based image retrieval by addressing three primary issues: image feature extraction and representation, similarity measure, and search methods. A statistically based feature selection scheme is introduced to guide the selection of the most appropriate image features for dynamic image indexing and similarity measures. In addition, a fractional discrimination function is proposed to enhance image feature points in conjunction with image decomposition and contextual filtering for image classification. Furthermore, a feature component code is used to facilitate the hierarchical search for the best matching, where images are queried by different features or combinations. The experimental results demonstrate the effectiveness of the proposed method.

Original language	English
Title of host publication	IEEE International Conference on Image Processing
Pages	717-720
Number of pages	4
Publication status	Published - 1 Jan 2001
Externally published	Yes
Event	IEEE International Conference on Image Processing (ICIP) - Thessaloniki, Greece Duration: 7 Oct 2001 → 10 Oct 2001

Conference

Conference	IEEE International Conference on Image Processing (ICIP)
Country/Territory	Greece
City	Thessaloniki
Period	7/10/01 → 10/10/01

ASJC Scopus subject areas

Hardware and Architecture
Computer Vision and Pattern Recognition
Electrical and Electronic Engineering

Cite this

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title = "Feature guide: A statistically based feature selection scheme",

abstract = "This paper presents a new approach to content-based image retrieval by addressing three primary issues: image feature extraction and representation, similarity measure, and search methods. A statistically based feature selection scheme is introduced to guide the selection of the most appropriate image features for dynamic image indexing and similarity measures. In addition, a fractional discrimination function is proposed to enhance image feature points in conjunction with image decomposition and contextual filtering for image classification. Furthermore, a feature component code is used to facilitate the hierarchical search for the best matching, where images are queried by different features or combinations. The experimental results demonstrate the effectiveness of the proposed method.",

author = "Jia You and T. Dillon and E. Pissaloux",

year = "2001",

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booktitle = "IEEE International Conference on Image Processing",

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T1 - Feature guide: A statistically based feature selection scheme

AU - You, Jia

AU - Dillon, T.

AU - Pissaloux, E.

PY - 2001/1/1

Y1 - 2001/1/1

N2 - This paper presents a new approach to content-based image retrieval by addressing three primary issues: image feature extraction and representation, similarity measure, and search methods. A statistically based feature selection scheme is introduced to guide the selection of the most appropriate image features for dynamic image indexing and similarity measures. In addition, a fractional discrimination function is proposed to enhance image feature points in conjunction with image decomposition and contextual filtering for image classification. Furthermore, a feature component code is used to facilitate the hierarchical search for the best matching, where images are queried by different features or combinations. The experimental results demonstrate the effectiveness of the proposed method.

AB - This paper presents a new approach to content-based image retrieval by addressing three primary issues: image feature extraction and representation, similarity measure, and search methods. A statistically based feature selection scheme is introduced to guide the selection of the most appropriate image features for dynamic image indexing and similarity measures. In addition, a fractional discrimination function is proposed to enhance image feature points in conjunction with image decomposition and contextual filtering for image classification. Furthermore, a feature component code is used to facilitate the hierarchical search for the best matching, where images are queried by different features or combinations. The experimental results demonstrate the effectiveness of the proposed method.

UR - http://www.scopus.com/inward/record.url?scp=0035164013&partnerID=8YFLogxK

M3 - Conference article published in proceeding or book

SP - 717

EP - 720

BT - IEEE International Conference on Image Processing

T2 - IEEE International Conference on Image Processing (ICIP)

Y2 - 7 October 2001 through 10 October 2001

ER -

Feature guide: A statistically based feature selection scheme

Abstract

Conference

ASJC Scopus subject areas

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