NDLI: Multimodal Data Mining in a Multimedia Database Based on Structured Max Margin Learning

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ACM Transactions on Knowledge Discovery from Data (TKDD) : Volume 11

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Issue 4(Special Issue on SIGKDD 2014, Special Issue on BIGCHAT and Regular Papers), July 2016

Issue 3, February 2016

Mining User Development Signals for Online Community Churner Detection

Put Three and Three Together: Triangle-Driven Community Detection

Multimodal Data Mining in a Multimedia Database Based on Structured Max Margin Learning

Do Anesthesiologists Know What They Are Doing? Mining a Surgical Time-Series Database to Correlate Expert Assessment with Outcomes

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Mining Influencers Using Information Flows in Social Streams

Toward Generalizing the Unification with Statistical Outliers: The Gradient Outlier Factor Measure

DeltaCon: Principled Massive-Graph Similarity Function with Attribution

Mining Product Adopter Information from Online Reviews for Improving Product Recommendation

Adaptive Model Rules From High-Speed Data Streams

Synchronization-Core-Based Discovery of Processes with Decomposable Cyclic Dependencies

An Efficient Algorithm For Weak Hierarchical Lasso

Issue 2, October 2015

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ACM Transactions on Knowledge Discovery from Data (TKDD) : Volume 9

ACM Transactions on Knowledge Discovery from Data (TKDD) : Volume 8

ACM Transactions on Knowledge Discovery from Data (TKDD) : Volume 7

ACM Transactions on Knowledge Discovery from Data (TKDD) : Volume 6

ACM Transactions on Knowledge Discovery from Data (TKDD) : Volume 5

ACM Transactions on Knowledge Discovery from Data (TKDD) : Volume 4

ACM Transactions on Knowledge Discovery from Data (TKDD) : Volume 3

ACM Transactions on Knowledge Discovery from Data (TKDD) : Volume 2

ACM Transactions on Knowledge Discovery from Data (TKDD) : Volume 1

Multimodal Data Mining in a Multimedia Database Based on Structured Max Margin Learning

Content Provider	ACM Digital Library
Author	Zhang, Zhongfei (Mark) Xing, Eric P. Guo, Zhen Faloutsos, Christos
Copyright Year	2016
Abstract	Mining knowledge from a multimedia database has received increasing attentions recently since huge repositories are made available by the development of the Internet. In this article, we exploit the relations among different modalities in a multimedia database and present a framework for general multimodal data mining problem where image annotation and image retrieval are considered as the special cases. Specifically, the multimodal data mining problem can be formulated as a structured prediction problem where we learn the mapping from an input to the structured and interdependent output variables. In addition, in order to reduce the demanding computation, we propose a new max margin structure learning approach called Enhanced Max Margin Learning (EMML) framework, which is much more efficient with a much faster convergence rate than the existing max margin learning methods, as verified through empirical evaluations. Furthermore, we apply EMML framework to develop an effective and efficient solution to the multimodal data mining problem that is highly scalable in the sense that the query response time is independent of the database scale. The EMML framework allows an efficient multimodal data mining query in a very large scale multimedia database, and excels many existing multimodal data mining methods in the literature that do not scale up at all. The performance comparison with a state-of-the-art multimodal data mining method is reported for the real-world image databases.
Starting Page	1
Ending Page	30
Page Count	30
File Format	PDF
ISSN	15564681
e-ISSN	1556472X
DOI	10.1145/2742549
Volume Number	10
Issue Number	3
Journal	ACM Transactions on Knowledge Discovery from Data (TKDD)
Language	English
Publisher	Association for Computing Machinery (ACM)
Publisher Date	2016-02-24
Publisher Place	New York
Access Restriction	One Nation One Subscription (ONOS)
Subject Keyword	Multimodal data mining Image annotation Image retrieval Max margin
Content Type	Text
Resource Type	Article
Subject	Computer Science

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

Enhanced max margin learning on multimodal data mining in a multimedia database

Enhanced max margin learning on multimodal data mining in a multimedia database (2007)

Structured Max Margin Learning on Image Annotation and Multimodal Image Retrieval

A MAX MARGIN FRAMEWORK ON IMAGE ANNOTATION AND MULTIMODAL IMAGE RETRIEVAL ∗

Enhanced max margin learning on multimodal data mining in a multimedia database (2007)

A max margin framework on image annotation and multimodal image retrieval ∗.

Multimodal Image Retrieval Based on Annotation Keywords and Visual Content

A MAX MARGIN FRAMEWORK ON IMAGE ANNOTATION AND MULTIMODAL IMAGE RETRIEVAL ∗

Active learning in multimedia annotation and retrieval: A survey

Multimodal Data Mining in a Multimedia Database Based on Structured Max Margin Learning

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