NDLI: Performance analysis of classification algorithms applied to Caltech101 image database

Content Provider	IEEE Xplore Digital Library
Author	Ajeesh, S.S. Indu, M.S. Sherly, E.
Copyright Year	2014
Description	Author affiliation: IIITM-K, Trivandrum, India (Sherly, E.) \|\| M.G. Univ., Kottayam, India (Indu, M.S.) \|\| M.S. Univ., Tirunelveli, India (Ajeesh, S.S.)
Abstract	Identifying the wide range of applications, machine learning algorithms proved its ability to learn without being explicitly programmed. Classifying the images through machine learning algorithms is getting wide range of acceptability nowadays. Being a branch of Artificial Intelligence, machine learning implies the study of systems which has the capability to learn from data. Machine learning involves two parts - representation and generalization. Representation implies labeling seen data instances and generalization determines whether the system can perform well on unlabelled data instances. In this article, we focused on the performance of machine learning algorithms [1]. A CBIR (Content Based Image Retrieval) frame work has been developed and obtained a reduced texture feature data set using Caltech101 image database [2]. We highlight the top five algorithms such as Logistic, Bagging, LMT, Multiclass classifier and Attribute selection classifier which can be used for image classification. In introduction, an overview of the selected techniques is presented. We have extracted 2037 feature vectors from Caltech101 image database. These data are used to distinguish the performance of machine learning algorithms. Having checked all machine learning algorithms supported, we identified top five algorithms that have a better performance compared to other machine learning algorithms. The software used for testing is WEKA [3], which is an open source software developed by University of Waikato, New Zealand.
Sponsorship	IEEE Delhi Sect.
Starting Page	693
Ending Page	696
File Size	390470
Page Count	4
File Format	PDF
e-ISBN	9781479929009
DOI	10.1109/ICICICT.2014.6781364
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2014-02-07
Publisher Place	India
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Biometrics (access control) Biological system modeling Caltech 101 Artificial Intelligence Representation Labeling Generalization Machine Learning Regression tree analysis Bagging Logistics
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

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