5M: Multi-Instance Multi-Cluster based Weakly Supervised MIL Model for Multimedia Data Mining

5M: Multi-Instance Multi-Cluster based Weakly Supervised MIL Model for Multimedia Data Mining

Girisha GS

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Dr. K. Udaya Kumar

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α Visvesvaraya Technological University

5M: Multi-Instance Multi-Cluster based Weakly Supervised MIL Model for Multimedia Data Mining

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Abstract

The high pace rise in online as well as offline multimedia un annotated data and associated mining applications have demanded certain efficient mining algorithm. Multiple instance learning (MIL) has emerged as one of the most effective solutions for huge un annotated data mining. Still, it requires enhancement in instance selection to enable optimal mining and classification of huge multimedia data. Considering critical multimedia mining applications, such as medical data processing or content based information retrieval, the instance verification can be of great significance to optimize MIL. With this motivation, in this paper, Multi-Instance, Multi-Cluster based MIL scheme (MIMC-MIL) has been proposed to perform efficient multimedia data mining and classification with huge un annotated data with different features. The proposed system employs soft max approximation techniques with a novel loss factor and inter-instance distance based weight estimation scheme for instance probability substantiation in bags.

References

29 Cites in Article

Reference Format

T Dietterich,R Lathrop,T Lozano-P´erez (1997). Solving the multiple instance problem with axisparallel rectangles.
S Ndrews,I Tsochantaridis,T Hofmann (2003). Support vector machines for multiple-instance learning.
Q Zhang,S Goldman,W Yu,J Fritts (2002). Content-based image retrieval using multiple instance learning.
Y Chen,J Bi,J Wang (2006). MILES: Multipleinstance learning via embedded instance selection.
Y Chen,J Wang (2004). Categorization by Learning and Reasoning with Regions.
Chih-Chung Chang,Chih-Jen Lin (2001). LIBSVM.
B Babenko,M Yang,S Belongie (2009). Visual tracking with online multiple instance learning.
Christian Leistner,Amir Saffari,Horst Bischof (2010). MIForests: Multiple-Instance Learning with Randomized Trees.
Gabriela Csurka,Christopher Dance,Florent Perronnin,Jutta Willamowski (2004). Generic Visual Categorization Using Weak Geometry.
Q Zhang,S Goldman (2002). Maps.
T G¨artner,P Flach,A Kowalczyk,A Smola (2002). Multi-instance kernels.
M Li,J Kwok,B Lu (2010). Online multiple instance learning with no regret.
Hua-Yan Wang,Qiang Yang,Hongbin Zha (2000). Adaptive p-posterior mixture-model kernels for multiple instance learning.
P Viola,J Platt,C Zhang (2006). Multiple instance boosting for object detection.
W Li,D Yeung (2010). MILD: Multiple-instance learning via disambiguation.
Z Fu,A Robles-Kelly,J Zhou (2010). MILIS: Multiple instance learning with instance selection.
D Zhang,F Wang,L Si,T Li (2009). M3IC: maximum margin multiple instance clustering.
Zhi-Hua Zhou,Min-Ling Zhang (2007). Multi-Instance Multi-Label Learning with Application to Scene Classification.
Z.-J Zha,T Mei,J Wang,G.-J Qi,Z Wang (2008). Joint multi-label multi instance learning for image classification.
P Viola,J Platt,C Zhang (2005). Multiple instance boosting for object detection.
Llew Mason,Jonathan Baxter,Peter Bartlett,Marcus Frean (2000). Functional Gradient Techniques for Combining Hypotheses.
B Babenko,P Doll´ar,Z Tu,S Belongie (2008). Simultaneous learning and alignment: Multi-instance and multi-pose learning.
A Vedaldi,V Gulshan,M Varma,A Zisserman (2009). Multiple kernels for object detection.
G Girisha,K Kumar (2015). An Enhanced Semi-Supervised Multiple Instance Learning Scheme for Multimedia Data Mining.
Daniel Kelly,John Mc Donald,Charles Markham (2011). Weakly Supervised Training of a Sign Language Recognition System Using Multiple Instance Learning Density Matrices.
H Cheng,I Gondra (2010). A Novel Neural Network-Based Approach for Multiple Instance Learning.
X Yuan,M Wang,Y Song (2009). Concept-dependent image annotation via existence-based multipleinstance learning.
W Dijia,B Jinbo,K Boyer (2009). A min-max framework of cascaded classifier with multiple instance learning for computer aided diagnosis.
P Doll´ar,B Babenko,S Belongie,P Perona,Z Tu (2008). Multiple component learning for object detection.

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Funding

No external funding was declared for this work.

Conflict of Interest

The authors declare no conflict of interest.

Ethical Approval

No ethics committee approval was required for this article type.

Data Availability

Not applicable for this article.

How to Cite This Article

Girisha GS. 2016. \u201c5M: Multi-Instance Multi-Cluster based Weakly Supervised MIL Model for Multimedia Data Mining\u201d. Global Journal of Computer Science and Technology - C: Software & Data Engineering GJCST-C Volume 16 (GJCST Volume 16 Issue C3): .

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GJCST Volume 16 Issue C3
Pg. 21- 28

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Journal Specifications

Crossref Journal DOI 10.17406/gjcst

Print ISSN 0975-4350

e-ISSN 0975-4172

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Classification

GJCST-C Classification: H.2.4 H.2.8

Submission ReceivedDecember 16, 2015
Peer Review Double Blind
Handling Editor
Accepted January 3, 2016
Published January 15, 2016

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v1.2

Issue date

July 1, 2016

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Article Matrices

Total Score: 107

Country: India

Subject: Global Journal of Computer Science and Technology - C: Software & Data Engineering

Authors: Girisha GS, Dr. K. Udaya Kumar (PhD/Dr. count: 1)

View Count (all-time): 290

Total Views (Real + Logic): 7371

Total Downloads (simulated): 1894

Publish Date: 2016 07, Fri

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Total Views: 7371

Total Downloads: 1894

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