NDLI: Image Categorization with PCA-SICEF

Content Provider	IEEE Xplore Digital Library
Author	Okamoto, A. Xianhua Han Xiang Ruan Yen-Wei Chen
Copyright Year	2009
Abstract	Image category recognition is important to access visual information on the level of objects and scene types. This paper presents an automatic recognition system of scene and object with PCA-SICEF feature for digital color images. SICEF (Scale-Invariant Color and edge Feature) is an extension of the conventional local SIFT (Scale-Invariant Feature transform) feature,which only include edge invariance of local image region but not any color information. So the SIFT feature is not enough for distinguish image categorization especially for scene types, where the color information plays an important role for recognition. Therefore, we improve SIFT by including color feature for local image region, and name it as SICEF feature. However, the Dimension of the extracted SICEF feature is so high that we use PCA(Principle Component Analysis) to reduce the dimension, and then, use the PCA-domain SICEF (PCA-SICEF) for image classification. Experimental results show that it is much more efficient by our proposed PCA-SICEF feature than conventional SIFT feature.
Starting Page	31
Ending Page	35
File Size	1220015
Page Count	5
File Format	PDF
ISBN	9780769537368
DOI	10.1109/ICNC.2009.655
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2009-08-14
Publisher Place	China
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Image recognition SIFT Data mining Object recognition PCA Histograms Image analysis Layout Horses local feature Feature extraction image categorization SICEF Image classification Principal component 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

Feature extraction for object recognition using PCA-KNN with application to medical image analysis

Combining MSCR detector and PCA-SIFT descriptor for scene recognition

Dimensionality reduction of SIFT using PCA for object categorization

Feature Extraction Using Wavelet-PCA and Neural Network for Application of Object Classification & Face Recognition

Kernel Principal Component Analysis feature detection and classification for underwater images

Gender Classification Based on Enhanced PCA-SIFT Facial Features

Recognizing 3D Objects using Ray-Triangle Intersection Distances

Color and texture feature fusion using kernel PCA with application to object-based vegetation species classification

Based on local feature region fusion of facial expression recognition

Image Categorization with PCA-SICEF

Similar Documents

Feature extraction for object recognition using PCA-KNN with application to medical image analysis

Combining MSCR detector and PCA-SIFT descriptor for scene recognition

Dimensionality reduction of SIFT using PCA for object categorization

Feature Extraction Using Wavelet-PCA and Neural Network for Application of Object Classification & Face Recognition

Kernel Principal Component Analysis feature detection and classification for underwater images

Gender Classification Based on Enhanced PCA-SIFT Facial Features

Recognizing 3D Objects using Ray-Triangle Intersection Distances

Color and texture feature fusion using kernel PCA with application to object-based vegetation species classification

Based on local feature region fusion of facial expression recognition

Image Categorization with PCA-SICEF