NDLI: A Feature Selection Framework for Small Sampling Data in Content-based Image Retrieval System

Content Provider	IEEE Xplore Digital Library
Author	Kien-Ping Chung Chun Che Fung Kok Wai Wong
Copyright Year	2005
Description	Author affiliation: Sch. of Inf. Technol., Murdoch Univ., WA (Kien-Ping Chung; Chun Che Fung; Kok Wai Wong)
Abstract	Content-based image retrieval (CBIR) systems have drawn interest from many researchers in recent years. Over the last few years, kernel-based approach has been a popular choice for the implementation of the relevance feedback based CBIR system. This is largely due to its ability to classify patterns with limited sample data. A long flat vector has been a popular choice for the input configuration. The reasons are because it is relatively easy to implement and more importantly, because it preserve the information of identifying the target images via different combination of image features. However, one of the biggest weaknesses of such configuration is the "curse of dimensionality". This paper introduces a relevance feedback framework via the use of statistical discriminant analysis method to select only relevant feature for next image retrieval cycle. Hence, minimize the dimensionality of the feature vector. This approach has been tested with four sets of images labelled with different themes. Each set contains 500 images, 50 labelled as positive while the rest are negative. The test showed an improvement from the previous flat input vector configuration when the training samples are relatively small
Starting Page	310
Ending Page	314
File Size	1602131
Page Count	5
File Format	PDF
ISBN	0780392833
DOI	10.1109/ICICS.2005.1689057
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2005-12-06
Publisher Place	Thailand
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Image sampling Information retrieval Image retrieval Content based retrieval Feedback Image analysis Testing Information technology Explosions Internet Feature Selection Content-based image retrieval Relevance Feedback Statistical Discriminant Analysis
Content Type	Text
Resource Type	Article

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

W3A.3 A Feature Selection Framework for Small Sampling Data in Content-based Image Retrieval System

A hierarchical discriminant analysis framework for content-based image retrieval system for industrial applications

Multiple Layar Kernel-Based Approach in Relevance Feedback Content-Based Image Retrieval System

Feature selection and re-weighting in content-based SAR image retrieval

Feature Relevance Learning in Content-Based Image Retrieval Using GRA

Relevance feedback techniques for image retrieval using multiple attributes

A Hierarchical Discriminant Analysis Framework for Content-based Image Retrieval System for Industrial Applications

A hierarchical nonparametric discriminant analysis approach for a content-based image retrieval system

A Hybrid PSO and Active Learning SVM Model for Relevance Feedback in the Content-Based Images Retrieval

A Feature Selection Framework for Small Sampling Data in Content-based Image Retrieval System

Similar Documents

W3A.3 A Feature Selection Framework for Small Sampling Data in Content-based Image Retrieval System

A hierarchical discriminant analysis framework for content-based image retrieval system for industrial applications

Multiple Layar Kernel-Based Approach in Relevance Feedback Content-Based Image Retrieval System

Feature selection and re-weighting in content-based SAR image retrieval

Feature Relevance Learning in Content-Based Image Retrieval Using GRA

Relevance feedback techniques for image retrieval using multiple attributes

A Hierarchical Discriminant Analysis Framework for Content-based Image Retrieval System for Industrial Applications

A hierarchical nonparametric discriminant analysis approach for a content-based image retrieval system

A Hybrid PSO and Active Learning SVM Model for Relevance Feedback in the Content-Based Images Retrieval

A Feature Selection Framework for Small Sampling Data in Content-based Image Retrieval System