NDLI: Multi-instance Learning Using Information Entropy Theory for Image Retrieval

Content Provider	IEEE Xplore Digital Library
Author	Junyi, Li Jianhua, Li Shuicheng, Yan
Copyright Year	2014
Abstract	As a new learning framework, Multi-Instance learning is used successfully in vision classification and labeled recently. In this paper, a novel Multi-instance bag generating method is put forward on the basis of a Gaussian Mixed Model. The generated GMM model composes not only color but also the locally stable unchangeable components. It is called MI bag by researchers. Besides this, another method which is called Agglomerative Information Bottleneck clustering is ad opted here to replace the MIL problem with the help of single-instance learning ones. Meanwhile, single-instance classifiers are employed here for classification. Finally, ensemble learning is employed to strengthen classifiers' generalization ability of RBM (Restricted Boltzmann Machine) as the base classifier. On the basis of large-scale datasets, this method is tested and the result of it shows that our method provides higher accuracy and performance for image annotation, feature matching and example-based object-classification.
Starting Page	1727
Ending Page	1733
File Size	681318
Page Count	7
File Format	PDF
e-ISBN	9781479979813
DOI	10.1109/CSE.2014.317
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2014-12-19
Publisher Place	China
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Gaussian distribution Multi-Instance Learning Classification algorithms Scene Recognition Training Image color analysis Gaussian Mixed Model Clustering algorithms Image representation AIB Clustering RBM Mutual information Image classification
Content Type	Text
Resource Type	Article

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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
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