NDLI: Phase congruency and ODBTC based image retrieval

Content Provider	IET Digital Library
Author	Gupta, Shraddha Modem, Sudhakar Thakre, Vandana Vikas
Abstract	In this study, the authors investigate content based image retrieval (CBIR) using ordered-dither block truncation coding (ODBTC) and phase congruency feature (PCF). Relevant feature extraction plays a vital role for retrieving the image in CBIR. The unique reason to choose PCF with ODBTC is that it detects the edges and corners during variation of image while preserving image brightness and contrast. Combining the PCF and ODBTC features improves CBIR system usage in various visual data processing domains. Thus, yields a better CBIR system which assists in the reduction of storage space, decreases retrieval time and increases accuracy of the system. The precision and recall are used as performance metrics to evaluate the proposed method based on retrieval of relevant images. Extensive experimental results with Corel 1 K (1000 images), Corel 10 K (10000 images) and CALTECH 256 (30144 images) proves that the proposed method is more desirable than antecedent proposed CBIR system in terms of accuracy, precision and recall.
Starting Page	2195
Ending Page	2203
Page Count	9
ISSN	17519659
Volume Number	14
e-ISSN	17519667
Issue Number	Issue 10, Aug (2020)
Alternate Webpage(s)	https://digital-library.theiet.org/content/journals/iet-ipr/14/10
Alternate Webpage(s)	https://digital-library.theiet.org/content/journals/10.1049/iet-ipr.2019.1023
Journal	IET Image Processing
Publisher Date	2020-04-02
Access Restriction	Open
Rights Holder	© The Institution of Engineering and Technology
Subject Keyword	Block Codes CBIR System Computer Vision And Image Processing Technique Content Based Image Retrieval Content-based Retrieval Corner Detection Edge Detection Feature Extraction Image And Video Coding Image Brightness Image Coding Image Contrast Image Recognition Image Retrieval Information Retrieval Technique ODBTC Based Image Retrieval ODBTC Feature Ordered-dither Block Truncation Coding PCF Performance Metrics Phase Congruency Feature Storage Space Reduction Temperature 1.0 K Temperature 10.0 K Visual Data Processing Domain
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

Content-Based Image Retrieval Using Features Extracted From Halftoning-Based Block Truncation Coding

Content-Based Image Retrieval Using Error Diffusion Block Truncation Coding Features

New strategy for CBIR by combining low-level visual features with a colour descriptor

New non-negative sparse feature learning approach for content-based image retrieval

New content-based image retrieval system based on optimised integration of DCD, wavelet and curvelet features

Effective key-frame extraction approach using TSTBTC–BBA

Detected text-based image retrieval approach for textual images

Content-based image retrieval using fuzzy class membership and rules based on classifier confidence

Content-based image retrieval system via sparse representation

Phase congruency and ODBTC based image retrieval

Similar Documents

Content-Based Image Retrieval Using Features Extracted From Halftoning-Based Block Truncation Coding

Content-Based Image Retrieval Using Error Diffusion Block Truncation Coding Features

New strategy for CBIR by combining low-level visual features with a colour descriptor

New non-negative sparse feature learning approach for content-based image retrieval

New content-based image retrieval system based on optimised integration of DCD, wavelet and curvelet features

Effective key-frame extraction approach using TSTBTC–BBA

Detected text-based image retrieval approach for textual images

Content-based image retrieval using fuzzy class membership and rules based on classifier confidence

Content-based image retrieval system via sparse representation

Phase congruency and ODBTC based image retrieval