NDLI: A sparse kernel relevance model for automatic image annotation

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A sparse kernel relevance model for automatic image annotation

Content Provider	Springer Nature Link
Author	Lavrenko, Victor Moran, Sean
Copyright Year	2014
Abstract	In this paper, we introduce a new form of the continuous relevance model (CRM), dubbed the SKL-CRM, that adaptively selects the best performing kernel per feature type for automatic image annotation. Previous image annotation models apply a standard selection of kernels to model the distribution of image features. Popular examples include a Gaussian kernel for modelling GIST features or a Laplacian kernel for global colour histograms. In this work, we demonstrate that this standard assignment of kernels to feature types is sub-optimal and a substantially higher image annotation accuracy can be attained by adapting the kernel-feature assignment. We formulate an efficient greedy algorithm to find the best kernel-feature alignment and show that it is able to rapidly find a sparse subset of features that maximises annotation $$F_{1}$$ score. In a second contribution, we introduce two data-adaptive kernels for image annotation—the generalised Gaussian and multinomial kernels—which we demonstrate can better model the distribution of image features as compared to standard kernels. Evaluation is conducted on three standard image datasets across a selection of different feature representations. The proposed SKL-CRM model is found to attain performance that is competitive to a suite of state-of-the-art image annotation models.
Starting Page	209
Ending Page	229
Page Count	21
File Format	PDF
ISSN	21926611
Journal	International Journal of Multimedia Information Retrieval
Volume Number	3
Issue Number	4
e-ISSN	2192662X
Language	English
Publisher	Springer London
Publisher Date	2014-09-19
Publisher Place	London
Access Restriction	One Nation One Subscription (ONOS)
Subject Keyword	Image annotation Information Storage and Retrieval Data Mining and Knowledge Discovery Image Processing and Computer Vision Computer Science Information Systems Applications (incl. Internet) Multimedia Information Systems Object recognition Kernel density estimation
Content Type	Text
Resource Type	Article
Subject	Library and Information Sciences Information Systems Media Technology

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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
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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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