NDLI: Flip-Invariant SIFT for Copy and Object Detection

Content Provider	IEEE Xplore Digital Library
Author	Wan-Lei Zhao Chong-Wah Ngo
Copyright Year	1992
Abstract	Scale-invariant feature transform (SIFT) feature has been widely accepted as an effective local keypoint descriptor for its invariance to rotation, scale, and lighting changes in images. However, it is also well known that SIFT, which is derived from directionally sensitive gradient fields, is not flip invariant. In real-world applications, flip or flip-like transformations are commonly observed in images due to artificial flipping, opposite capturing viewpoint, or symmetric patterns of objects. This paper proposes a new descriptor, named flip-invariant SIFT (or F-SIFT), that preserves the original properties of SIFT while being tolerant to flips. F-SIFT starts by estimating the dominant curl of a local patch and then geometrically normalizes the patch by flipping before the computation of SIFT. We demonstrate the power of F-SIFT on three tasks: large-scale video copy detection, object recognition, and detection. In copy detection, a framework, which smartly indices the flip properties of F-SIFT for rapid filtering and weak geometric checking, is proposed. F-SIFT not only significantly improves the detection accuracy of SIFT, but also leads to a more than 50% savings in computational cost. In object recognition, we demonstrate the superiority of F-SIFT in dealing with flip transformation by comparing it to seven other descriptors. In object detection, we further show the ability of F-SIFT in describing symmetric objects. Consistent improvement across different kinds of keypoint detectors is observed for F-SIFT over the original SIFT.
Sponsorship	IEEE Signal Processing Society
Page Count	12
File Size	1341094
Starting Page	980
Ending Page	991
File Format	PDF
ISSN	10577149
Volume Number	22
Issue Number	3
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2013-01-01
Publisher Place	U.S.A.
Access Restriction	One Nation One Subscription (ONOS)
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Detectors Feature extraction Object detection Clocks Histograms Vectors Object recognition video copy detection Flip invariant scale-invariant feature transform (SIFT) geometric verification object detection
Content Type	Text
Resource Type	Article
Subject	Computer Graphics and Computer-Aided Design 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

$n$ -SIFT: $n$ -Dimensional Scale Invariant Feature Transform

A new SIFT keypoint descriptor for copy detection

Robust Video Object Cosegmentation

Image Feature Extraction in Encrypted Domain With Privacy-Preserving SIFT

High-Performance SIFT Hardware Accelerator for Real-Time Image Feature Extraction

Scale-Invariant Features for 3-D Mesh Models

LBP-Based Edge-Texture Features for Object Recognition

Data-Driven Hierarchical Structure Kernel for Multiscale Part-Based Object Recognition

Fast SIFT Design for Real-Time Visual Feature Extraction

Flip-Invariant SIFT for Copy and Object Detection

Similar Documents

$n$ -SIFT: $n$ -Dimensional Scale Invariant Feature Transform

A new SIFT keypoint descriptor for copy detection

Robust Video Object Cosegmentation

Image Feature Extraction in Encrypted Domain With Privacy-Preserving SIFT

High-Performance SIFT Hardware Accelerator for Real-Time Image Feature Extraction

Scale-Invariant Features for 3-D Mesh Models

LBP-Based Edge-Texture Features for Object Recognition

Data-Driven Hierarchical Structure Kernel for Multiscale Part-Based Object Recognition

Fast SIFT Design for Real-Time Visual Feature Extraction

Flip-Invariant SIFT for Copy and Object Detection