NDLI: CPMC: Automatic Object Segmentation Using Constrained Parametric Min-Cuts

Content Provider	IEEE Xplore Digital Library
Author	Carreira, J. Sminchisescu, C.
Copyright Year	1979
Abstract	We present a novel framework to generate and rank plausible hypotheses for the spatial extent of objects in images using bottom-up computational processes and mid-level selection cues. The object hypotheses are represented as figure-ground segmentations, and are extracted automatically, without prior knowledge of the properties of individual object classes, by solving a sequence of Constrained Parametric Min-Cut problems (CPMC) on a regular image grid. In a subsequent step, we learn to rank the corresponding segments by training a continuous model to predict how likely they are to exhibit real-world regularities (expressed as putative overlap with ground truth) based on their mid-level region properties, then diversify the estimated overlap score using maximum marginal relevance measures. We show that this algorithm significantly outperforms the state of the art for low-level segmentation in the VOC 2009 and 2010 data sets. In our companion papers [1], [2], we show that the algorithm can be used, successfully, in a segmentation-based visual object category recognition pipeline. This architecture ranked first in the VOC2009 and VOC2010 image segmentation and labeling challenges.
Sponsorship	IEEE Computer Society
Page Count	17
File Size	4579483
Starting Page	1312
Ending Page	1328
File Format	PDF
ISSN	01628828
Volume Number	34
Issue Number	7
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2012-07-01
Publisher Place	U.S.A.
Access Restriction	One Nation One Subscription (ONOS)
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Image segmentation Image edge detection Image color analysis Detectors Object recognition Object segmentation Shape learning. figure-ground segmentation
Content Type	Text
Resource Type	Article
Subject	Applied Mathematics Artificial Intelligence Computational Theory and Mathematics Computer Vision and Pattern Recognition Software

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

Selection and Fusion of Color Models for Image Feature Detection

Layered Object Models for Image Segmentation

Measuring the Objectness of Image Windows

Edge and corner detection by photometric quasi-invariants

Rigid Shape Matching by Segmentation Averaging

Edge, junction, and corner detection using color distributions

Deformable shape detection and description via model-based region grouping

Groups of Adjacent Contour Segments for Object Detection

A locally constrained watershed transform

CPMC: Automatic Object Segmentation Using Constrained Parametric Min-Cuts

Similar Documents

Selection and Fusion of Color Models for Image Feature Detection

Layered Object Models for Image Segmentation

Measuring the Objectness of Image Windows

Edge and corner detection by photometric quasi-invariants

Rigid Shape Matching by Segmentation Averaging

Edge, junction, and corner detection using color distributions

Deformable shape detection and description via model-based region grouping

Groups of Adjacent Contour Segments for Object Detection

A locally constrained watershed transform

CPMC: Automatic Object Segmentation Using Constrained Parametric Min-Cuts