NDLI: A object classification method using product of randomly selected elements

Content Provider	IEEE Xplore Digital Library
Author	Morimoto, Naoki Hotta, Kazuhiro
Copyright Year	2015
Description	Author affiliation: Department of Electrical and Electronic Engineering, Meijo University, Nagoya, Aichi, Japan (Morimoto, Naoki; Hotta, Kazuhiro)
Abstract	In recent years, we treat a lot of images easily because of the spread of smart phones, internet and digital cameras, etc. To search or put tags to images automatically, an object classification method with high accuracy is required and many object classification methods have been proposed. Recent some papers demonstrated the effectiveness of features obtained by the CNN trained from 1000 categories in the ImageNet. To improve the accuracy of features obtained by the pretrained CNN, we propose to use the combination of elements selected randomly from a feature vector obtained by the pretrained CNN. We do not know which combination is effective for a classification task. Thus, we compute many combinations of two or three randomly selected elements and trained a linear SVM with the combination feature. Only the combinations with large absolute weight of SVM are selected and used for final classification. In the experiments using the CIFAR10 dataset, we confirmed that accuracy is improved by the combination of randomly selected elements.
Starting Page	428
Ending Page	432
File Size	488668
Page Count	5
File Format	PDF
e-ISBN	9781467372428
DOI	10.1109/SII.2015.7405017
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2015-12-11
Publisher Place	Japan
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Support vector machines Training Computer vision Neural networks Feature extraction Birds Pattern recognition
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 Simple Classification

Fast Training of Object Detection Using Stochastic Gradient Descent

Forest Species Recognition Using Deep Convolutional Neural Networks

Component-based face detection

Contextualizing object detection and classification

Comparison of artificial neural networks an support vector machines for feature selection in electrogastrography signal processing

An efficient three-stage classifier for handwritten digit recognition

Feature context for image classification and object detection

View-based 3D object recognition with support vector machines

A object classification method using product of randomly selected elements

Similar Documents

Feature Extraction for Simple Classification

Fast Training of Object Detection Using Stochastic Gradient Descent

Forest Species Recognition Using Deep Convolutional Neural Networks

Component-based face detection

Contextualizing object detection and classification

Comparison of artificial neural networks an support vector machines for feature selection in electrogastrography signal processing

An efficient three-stage classifier for handwritten digit recognition

Feature context for image classification and object detection

View-based 3D object recognition with support vector machines

A object classification method using product of randomly selected elements