NDLI: Winning-weighted competitive learning for image compression

Content Provider	IEEE Xplore Digital Library
Author	Zhicheng Wang, J.
Copyright Year	1995
Description	Author affiliation: ATI Technol. Inc., Canada (Zhicheng Wang, J.)
Abstract	Summary form only given. The LBG algorithm and Kohonen learning algorithm (KLA) both have the problem of being trapped in local minima, the empty cell in LBG and the never winning codevector in KLA. Although compared to the LBG algorithm, Kohonen learning actually relaxes the initial limitation in design, it still relies on initial conditions. We point out the principle of maximum information preservation for estimation of an unknown probability density function and unsupervised learning. Accordingly, we introduce the winning-weighted competition in the design phase of vector quantizers and the corresponding implementation methods. The winning-weighted competitive learning (WWCL) proposed in this paper consists of the competition rule with the winning-weighted distortion measure d/sub p/(X,Y)=(1+/spl lambda/(p-1/N))/spl par/X-Y/spl par//sup 2/ instead of direct Euclidean distance, the win rate update pi(t+1)=pi(t)+/spl alpha//M where the competition status ci (winner or loser) of the ith codevector is zero or one, and the codevector learning law Y/sub i/(t+1)=Yi(t)+ci/spl alpha/(t)(X-Y/sub i/(t)). It allows neurons to win with roughly equal probability, achieves the corresponding approximate distribution of synaptic vectors for an input space, and eliminates the initial condition in vector quantizer design. The performance of our algorithm is usually better than Kohonen learning and the LBG algorithm in the sense of expected distortion and/or learning speed, and the global optima were obtained. Experimental results of the proposed learning scheme are presented and compared with the LBG algorithm and Kohonen learning algorithm.
File Size	76270
File Format	PDF
ISBN	0818670126
ISSN	10680314
DOI	10.1109/DCC.1995.515548
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	1995-03-28
Publisher Place	USA
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Image coding Algorithm design and analysis Probability density function Distortion measurement Vector quantization Partitioning algorithms Unsupervised learning Euclidean distance Neurons Testing
Content Type	Text
Resource Type	Article
Subject	Computer Networks and Communications

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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