NDLI: Randomized Clustering Forests for Image Classification

Content Provider	IEEE Xplore Digital Library
Author	Moosmann, F. Nowak, E. Jurie, F.
Copyright Year	1979
Abstract	Some of the most effective recent methods for content-based image classification work by quantizing image descriptors, and accumulating histograms of the resulting visual word codes. Large numbers of descriptors and large codebooks are required for good results and this becomes slow using k-means. We introduce Extremely Randomized Clustering Forests-ensembles of randomly created clustering trees-and show that they provide more accurate results, much faster training and testing, and good resistance to background clutter. Second, an efficient image classification method is proposed. It combines ERC-Forests and saliency maps very closely with the extraction of image information. For a given image, a classifier builds a saliency map online and uses it to classify the image. We show in several state-of-the-art image classification tasks that this method can speed up the classification process enormously. Finally, we show that the proposed ERC-Forests can also be used very successfully for learning distance between images. The distance computation algorithm consists of learning the characteristic differences between local descriptors sampled from pairs of same or different objects. These differences are vector quantized by ERC-Forests and the similarity measure is computed from this quantization. The similarity measure has been evaluated on four very different datasets and always outperforms the state-of-the-art competitive approaches.
Sponsorship	IEEE Computer Society
Page Count	15
File Size	2478583
Starting Page	1632
Ending Page	1646
File Format	PDF
ISSN	01628828
Volume Number	30
Issue Number	9
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2008-09-01
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 classification Quantization Image sampling Image retrieval Machine learning Histograms Classification tree analysis Student members Computer Society Clustering algorithms Object recognition Computer vision
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

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