A framework for grid-based image retrieval

Charlie Dagli, Thomas S Huang

Research output: Contribution to journalConference article

Abstract

In this paper we present a grid-based framework for image retrieval. In order to represent the intricate composition of images, the grid-based approach partitions each image into blocks from which a feature representation is derived from the local low-level content. Since the background often dominates the subject in the foreground, a special query selection method was developed. It combines the salient region-of-interest/query-by-example paradigm with coarse segmentation to remove the irrelevant background regions. The proposed search method looks for similar features across all block positions and at several scales. Existing local grid-based methods are constrained by searching for objects in the same position as the query object. Using this framework, the spatial constraint can be eliminated, and steps toward scale invariance can be taken. Promising results show that the grid-based method performs better than global search.

Original languageEnglish (US)
Pages (from-to)1021-1024
Number of pages4
JournalProceedings - International Conference on Pattern Recognition
Volume2
StatePublished - Dec 17 2004
EventProceedings of the 17th International Conference on Pattern Recognition, ICPR 2004 - Cambridge, United Kingdom
Duration: Aug 23 2004Aug 26 2004

Fingerprint

Image retrieval
Invariance
Chemical analysis

ASJC Scopus subject areas

  • Computer Vision and Pattern Recognition

Cite this

A framework for grid-based image retrieval. / Dagli, Charlie; Huang, Thomas S.

In: Proceedings - International Conference on Pattern Recognition, Vol. 2, 17.12.2004, p. 1021-1024.

Research output: Contribution to journalConference article

@article{f66ee67655c34e55b96b01057bc95ee4,
title = "A framework for grid-based image retrieval",
abstract = "In this paper we present a grid-based framework for image retrieval. In order to represent the intricate composition of images, the grid-based approach partitions each image into blocks from which a feature representation is derived from the local low-level content. Since the background often dominates the subject in the foreground, a special query selection method was developed. It combines the salient region-of-interest/query-by-example paradigm with coarse segmentation to remove the irrelevant background regions. The proposed search method looks for similar features across all block positions and at several scales. Existing local grid-based methods are constrained by searching for objects in the same position as the query object. Using this framework, the spatial constraint can be eliminated, and steps toward scale invariance can be taken. Promising results show that the grid-based method performs better than global search.",
author = "Charlie Dagli and Huang, {Thomas S}",
year = "2004",
month = "12",
day = "17",
language = "English (US)",
volume = "2",
pages = "1021--1024",
journal = "Proceedings - International Conference on Pattern Recognition",
issn = "1051-4651",
publisher = "Institute of Electrical and Electronics Engineers Inc.",

}

TY - JOUR

T1 - A framework for grid-based image retrieval

AU - Dagli, Charlie

AU - Huang, Thomas S

PY - 2004/12/17

Y1 - 2004/12/17

N2 - In this paper we present a grid-based framework for image retrieval. In order to represent the intricate composition of images, the grid-based approach partitions each image into blocks from which a feature representation is derived from the local low-level content. Since the background often dominates the subject in the foreground, a special query selection method was developed. It combines the salient region-of-interest/query-by-example paradigm with coarse segmentation to remove the irrelevant background regions. The proposed search method looks for similar features across all block positions and at several scales. Existing local grid-based methods are constrained by searching for objects in the same position as the query object. Using this framework, the spatial constraint can be eliminated, and steps toward scale invariance can be taken. Promising results show that the grid-based method performs better than global search.

AB - In this paper we present a grid-based framework for image retrieval. In order to represent the intricate composition of images, the grid-based approach partitions each image into blocks from which a feature representation is derived from the local low-level content. Since the background often dominates the subject in the foreground, a special query selection method was developed. It combines the salient region-of-interest/query-by-example paradigm with coarse segmentation to remove the irrelevant background regions. The proposed search method looks for similar features across all block positions and at several scales. Existing local grid-based methods are constrained by searching for objects in the same position as the query object. Using this framework, the spatial constraint can be eliminated, and steps toward scale invariance can be taken. Promising results show that the grid-based method performs better than global search.

UR - http://www.scopus.com/inward/record.url?scp=10044291668&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=10044291668&partnerID=8YFLogxK

M3 - Conference article

AN - SCOPUS:10044291668

VL - 2

SP - 1021

EP - 1024

JO - Proceedings - International Conference on Pattern Recognition

JF - Proceedings - International Conference on Pattern Recognition

SN - 1051-4651

ER -