Automatic salient-object extraction using the contrast map and salient points

Soo Yeong Kwak, Byoung Chul Ko, Hyeran Byun

Research output: Contribution to journalArticle

15 Citations (Scopus)

Abstract

In this paper, we propose a salient object extraction method using the contrast map and salient points for object-based image retrieval. In order to make the contrast map, we generate three-feature maps such as luminance map, color map and orientation map and extract salient points from an image. By using these features, we can decide the Attention Window (AW) location easily. The purpose of the AW is to remove the useless regions included in the image such as background as well as reducing the amount of image processing. To create the exact location and flexible size of the AW, we use above features with some proposed rules instead of using pre-assumptions or heuristic parameters. After determining of the AW, we apply the image segmentation to inner area of the AW and combine the candidate salient regions as one salient object.

Original languageEnglish
Pages (from-to)138-145
Number of pages8
JournalLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume3332
Publication statusPublished - 2004 Dec 1

Fingerprint

Salient point
Luminance
Image retrieval
Image Retrieval
Image segmentation
Image Segmentation
Object
Image Processing
Image processing
Heuristics
Color

All Science Journal Classification (ASJC) codes

  • Theoretical Computer Science
  • Computer Science(all)

Cite this

@article{9567ad2c3ae3400597a6fff0c139c643,
title = "Automatic salient-object extraction using the contrast map and salient points",
abstract = "In this paper, we propose a salient object extraction method using the contrast map and salient points for object-based image retrieval. In order to make the contrast map, we generate three-feature maps such as luminance map, color map and orientation map and extract salient points from an image. By using these features, we can decide the Attention Window (AW) location easily. The purpose of the AW is to remove the useless regions included in the image such as background as well as reducing the amount of image processing. To create the exact location and flexible size of the AW, we use above features with some proposed rules instead of using pre-assumptions or heuristic parameters. After determining of the AW, we apply the image segmentation to inner area of the AW and combine the candidate salient regions as one salient object.",
author = "Kwak, {Soo Yeong} and Ko, {Byoung Chul} and Hyeran Byun",
year = "2004",
month = "12",
day = "1",
language = "English",
volume = "3332",
pages = "138--145",
journal = "Lecture Notes in Computer Science",
issn = "0302-9743",
publisher = "Springer Verlag",

}

TY - JOUR

T1 - Automatic salient-object extraction using the contrast map and salient points

AU - Kwak, Soo Yeong

AU - Ko, Byoung Chul

AU - Byun, Hyeran

PY - 2004/12/1

Y1 - 2004/12/1

N2 - In this paper, we propose a salient object extraction method using the contrast map and salient points for object-based image retrieval. In order to make the contrast map, we generate three-feature maps such as luminance map, color map and orientation map and extract salient points from an image. By using these features, we can decide the Attention Window (AW) location easily. The purpose of the AW is to remove the useless regions included in the image such as background as well as reducing the amount of image processing. To create the exact location and flexible size of the AW, we use above features with some proposed rules instead of using pre-assumptions or heuristic parameters. After determining of the AW, we apply the image segmentation to inner area of the AW and combine the candidate salient regions as one salient object.

AB - In this paper, we propose a salient object extraction method using the contrast map and salient points for object-based image retrieval. In order to make the contrast map, we generate three-feature maps such as luminance map, color map and orientation map and extract salient points from an image. By using these features, we can decide the Attention Window (AW) location easily. The purpose of the AW is to remove the useless regions included in the image such as background as well as reducing the amount of image processing. To create the exact location and flexible size of the AW, we use above features with some proposed rules instead of using pre-assumptions or heuristic parameters. After determining of the AW, we apply the image segmentation to inner area of the AW and combine the candidate salient regions as one salient object.

UR - http://www.scopus.com/inward/record.url?scp=35048897901&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=35048897901&partnerID=8YFLogxK

M3 - Article

AN - SCOPUS:35048897901

VL - 3332

SP - 138

EP - 145

JO - Lecture Notes in Computer Science

JF - Lecture Notes in Computer Science

SN - 0302-9743

ER -