NDLI: Unsupervised Salient Object Segmentation Based on Kernel Density Estimation and Two-Phase Graph Cut

Content Provider	IEEE Xplore Digital Library
Author	Zhi Liu Ran Shi Liquan Shen Yinzhu Xue King Ngi Ngan Zhaoyang Zhang
Copyright Year	1999
Abstract	In this paper, we propose an unsupervised salient object segmentation approach based on kernel density estimation (KDE) and two-phase graph cut. A set of KDE models are first constructed based on the pre-segmentation result of the input image, and then for each pixel, a set of likelihoods to fit all KDE models are calculated accordingly. The color saliency and spatial saliency of each KDE model are then evaluated based on its color distinctiveness and spatial distribution, and the pixel-wise saliency map is generated by integrating likelihood measures of pixels and saliency measures of KDE models. In the first phase of salient object segmentation, the saliency map based graph cut is exploited to obtain an initial segmentation result. In the second phase, the segmentation is further refined based on an iterative seed adjustment method, which efficiently utilizes the information of minimum cut generated using the KDE model based graph cut, and exploits a balancing weight update scheme for convergence of segmentation refinement. Experimental results on a dataset containing 1000 test images with ground truths demonstrate the better segmentation performance of our approach.
Sponsorship	IEEE Signal Processing Society IEEE Circuits and Systems Society IEEE Communications Society IEEE Computer Society
Starting Page	1275
Ending Page	1289
Page Count	15
File Size	3115802
File Format	PDF
ISSN	15209210
Volume Number	14
Issue Number	4
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2012-08-01
Publisher Place	U.S.A.
Access Restriction	One Nation One Subscription (ONOS)
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Object segmentation Image segmentation Image color analysis Kernel Estimation Educational institutions Biological system modeling spatial saliency Color saliency graph cut kernel density estimation saliency model salient object segmentation seed adjustment
Content Type	Text
Resource Type	Article
Subject	Signal Processing Electrical and Electronic Engineering Computer Science Applications Media Technology

Sl.	Authority	Responsibilities	Communication Details
1	Ministry of Education (GoI), Department of Higher Education	Sanctioning Authority	https://www.education.gov.in/ict-initiatives
2	Indian Institute of Technology Kharagpur	Host Institute of the Project: The host institute of the project is responsible for providing infrastructure support and hosting the project	https://www.iitkgp.ac.in
3	National Digital Library of India Office, Indian Institute of Technology Kharagpur	The administrative and infrastructural headquarters of the project	Dr. B. Sutradhar bsutra@ndl.gov.in
4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
5	Website/Portal (Helpdesk)	Queries regarding NDLI and its services	support@ndl.gov.in
6	Contents and Copyright Issues	Queries related to content curation and copyright issues	content@ndl.gov.in
7	National Digital Library of India Club (NDLI Club)	Queries related to NDLI Club formation, support, user awareness program, seminar/symposium, collaboration, social media, promotion, and outreach	clubsupport@ndl.gov.in
8	Digital Preservation Centre (DPC)	Assistance with digitizing and archiving copyright-free printed books	dpc@ndl.gov.in
9	IDR Setup or Support	Queries related to establishment and support of Institutional Digital Repository (IDR) and IDR workshops	idr@ndl.gov.in

A novel salient object segmentation based on density estimation.

Unsupervised salient object segmentation based on kernel density estimation and two-phase Graph Cut (2012)

Nonparametric saliency detection using kernel density estimation

Salient Object Detection through Over-Segmentation

Automatic segmentation of salient objects using iterative reversible graph cut

Object Co-Segmentation Based on Shortest Path Algorithm and Saliency Model

Fusing region contrast and graph regularization for saliency detection

Region Fusion and Grab-Cut Based Salient Object Segmentation

Salient Region Detection Using Patch Level and Region Level Image Abstractions

Unsupervised Salient Object Segmentation Based on Kernel Density Estimation and Two-Phase Graph Cut

Similar Documents

A novel salient object segmentation based on density estimation.

Unsupervised salient object segmentation based on kernel density estimation and two-phase Graph Cut (2012)

Nonparametric saliency detection using kernel density estimation

Salient Object Detection through Over-Segmentation

Automatic segmentation of salient objects using iterative reversible graph cut

Object Co-Segmentation Based on Shortest Path Algorithm and Saliency Model

Fusing region contrast and graph regularization for saliency detection

Region Fusion and Grab-Cut Based Salient Object Segmentation

Salient Region Detection Using Patch Level and Region Level Image Abstractions

Unsupervised Salient Object Segmentation Based on Kernel Density Estimation and Two-Phase Graph Cut