NDLI: Kernel Null Space Methods for Novelty Detection

Content Provider	IEEE Xplore Digital Library
Author	Bodesheim, P. Freytag, A. Rodner, E. Kemmler, M. Denzler, J.
Copyright Year	2013
Description	Author affiliation: Comput. Vision Group, Friedrich Schiller Univ. Jena, Jena, Germany (Bodesheim, P.; Freytag, A.; Rodner, E.; Kemmler, M.; Denzler, J.)
Abstract	Detecting samples from previously unknown classes is a crucial task in object recognition, especially when dealing with real-world applications where the closed-world assumption does not hold. We present how to apply a null space method for novelty detection, which maps all training samples of one class to a single point. Beside the possibility of modeling a single class, we are able to treat multiple known classes jointly and to detect novelties for a set of classes with a single model. In contrast to modeling the support of each known class individually, our approach makes use of a projection in a joint subspace where training samples of all known classes have zero intra-class variance. This subspace is called the null space of the training data. To decide about novelty of a test sample, our null space approach allows for solely relying on a distance measure instead of performing density estimation directly. Therefore, we derive a simple yet powerful method for multi-class novelty detection, an important problem not studied sufficiently so far. Our novelty detection approach is assessed in comprehensive multi-class experiments using the publicly available datasets Caltech-256 and Image Net. The analysis reveals that our null space approach is perfectly suited for multi-class novelty detection since it outperforms all other methods.
Starting Page	3374
Ending Page	3381
File Size	423095
Page Count	8
File Format	PDF
ISBN	9780769549897
ISSN	10636919
DOI	10.1109/CVPR.2013.433
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2013-06-23
Publisher Place	USA
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Null space Training Joints Feature extraction Transforms Support vector machines kernel methods novelty detection null space multi-class modeling subspace methods
Content Type	Text
Resource Type	Article
Subject	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

Contextualizing object detection and classification

Linear dependency modeling for feature fusion

Salient object detection via bootstrap learning

Feature shift detection

Compositional Models for Video Event Detection: A Multiple Kernel Learning Latent Variable Approach

Learning equivariant structured output SVM regressors

Learning Graph Cut Energy Functions for Image Segmentation

The NBNN kernel

Large-scale image classification: Fast feature extraction and SVM training

Kernel Null Space Methods for Novelty Detection

Similar Documents

Contextualizing object detection and classification

Linear dependency modeling for feature fusion

Salient object detection via bootstrap learning

Feature shift detection

Compositional Models for Video Event Detection: A Multiple Kernel Learning Latent Variable Approach

Learning equivariant structured output SVM regressors

Learning Graph Cut Energy Functions for Image Segmentation

The NBNN kernel

Large-scale image classification: Fast feature extraction and SVM training

Kernel Null Space Methods for Novelty Detection