NDLI: KSR-BOF: a new and exemplified method (as KSRs method) for image classification

Content Provider	IET Digital Library
Author	Maleki, Mohammad Hassan Hodtani, Ghosheh Abed Hashemi, Seyed Hesam Odin
Abstract	Image classification is very important in pattern recognition and computer vision, where, for integrating final representation, feature pooling methods of the max-pooling, sum-pooling and average-pooling have been widely used. In this study, the authors propose a new method called K-strongest responses (KSRs) on the dictionary atoms for integrating the coding coefficients to generate the final representation that is compared with the previous pooling methods, produces better performance for the image classification task. On the basis of the KSR method, to improve classification accuracy and generate more compact and discriminative final representation, a new framework consisting of two-part KSR and bag-of-features is proposed. To evaluate the performance of the proposed method and framework, they apply it to locality-constrained linear coding, linear distance coding and sparse coding by using two datasets from benchmarks of scene classification: 19-class satellite scene and UC Merced Land. The results show that the coding coefficients integrated by their method and framework are more discriminative than other methods.
Starting Page	853
Ending Page	861
Page Count	9
ISSN	17519659
Volume Number	14
e-ISSN	17519667
Issue Number	Issue 5, Apr (2020)
Alternate Webpage(s)	https://digital-library.theiet.org/content/journals/iet-ipr/14/5
Alternate Webpage(s)	https://digital-library.theiet.org/content/journals/10.1049/iet-ipr.2019.0613
Journal	IET Image Processing
Publisher Date	2019-11-27
Access Restriction	Open
Rights Holder	© The Institution of Engineering and Technology
Subject Keyword	19-class Satellite Scene Average-pooling Bag-of-features Classification Accuracy Coding Coefficient Compact Representation Computer Vision Computer Vision And Image Processing Technique Dictionary Atoms Discriminative Final Representation Feature Extraction Image And Video Coding Image Classification Image Classification Task Image Coding Image Recognition Image Representation K-strongest Responses KSR Method KSR-BOF Method Linear Code Linear Distance Coding Locality-constrained Linear Coding Max-pooling Pattern Recognition Pooling Method Scene Classification Sparse Coding Sum-pooling Two-part KSR UC Merced Land
Content Type	Text
Resource Type	Article
Subject	Signal Processing Electrical and Electronic Engineering Computer Vision and Pattern Recognition Software

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

BOSSA: Extended bow formalism for image classification

Hierarchical spatial pyramid max pooling based on SIFT features and sparse coding for image classification

Synthetic training samples for enhanced locality-constrained dictionary learning

Human action recognition using weighted pooling

Learning a discriminative dictionary for locality constrained coding and sparse representation

Scene text recognition by learning co-occurrence of strokes based on spatiality embedded dictionary

Weighted locality-constrained linear coding for lesion classification in CT images

A.: BOSSA: extended BoW formalism for image classification

Locality-constrained Linear Coding for image classification

KSR-BOF: a new and exemplified method (as KSRs method) for image classification

Similar Documents

BOSSA: Extended bow formalism for image classification

Hierarchical spatial pyramid max pooling based on SIFT features and sparse coding for image classification

Synthetic training samples for enhanced locality-constrained dictionary learning

Human action recognition using weighted pooling

Learning a discriminative dictionary for locality constrained coding and sparse representation

Scene text recognition by learning co-occurrence of strokes based on spatiality embedded dictionary

Weighted locality-constrained linear coding for lesion classification in CT images

A.: BOSSA: extended BoW formalism for image classification

Locality-constrained Linear Coding for image classification

KSR-BOF: a new and exemplified method (as KSRs method) for image classification