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General Information
    • ISSN: 2010-3700
    • Frequency: Bimonthly
    • DOI: 10.18178/IJMLC
    • Editor-in-Chief: Dr. Lin Huang
    • Executive Editor:  Ms. Cherry L. Chen
    • Abstracing/Indexing: Engineering & Technology Digital Library, Google Scholar, Crossref, ProQuest, Electronic Journals Library, DOAJ and EI (INSPEC, IET).
    • E-mail: ijmlc@ejournal.net
Editor-in-chief
Dr. Lin Huang
Metropolitan State University of Denver, USA
It's my honor to take on the position of editor in chief of IJMLC. We encourage authors to submit papers concerning any branch of machine learning and computing.
IJMLC 2012 Vol.2(6): 762-766 ISSN: 2010-3700
DOI: 10.7763/IJMLC.2012.V2.232

Object and Scene Recognition Based on Learning Probabilistic Latent Component Tree with Boosted Features

Masayasu Atsumi
Abstract—This paper proposes an object and scene categorization method based on the probabilistic latent component tree with boosted features. In this method, object classes are firstly obtained by clustering a set of object segments extracted from scene images in each scene category through the probabilistic latent component analysis with the variable number of classes. Then the probabilistic latent component tree with boosted features at its branch nodes is generated as a classification tree of all the object classes of all the scene categories followed by labeling object classes. Lastly, each scene category is characterized according to the composition of its labeled object classes. Object and scene recognition is simultaneously performed based on the probabilistic latent component tree search by using composite boosted features for the tree traversal. Through experiments by using images of plural categories in an image database, it is shown that performance of object and scene recognition is high and improved by using composite boosted features in the probabilistic latent component tree search.

Index Terms—Boosting, categorization, computer vision, probabilistic learning.

M. Atsumi is with the Department of Information Systems Science, Faculty of Engineering, Soka University, 1-236 Tangi, Hachioji, Tokyo, Japan (e-mail: matsumi@t.soka.ac.jp).

[PDF]

Cite:Masayasu Atsumi, "Object and Scene Recognition Based on Learning Probabilistic Latent Component Tree with Boosted Features," International Journal of Machine Learning and Computing vol.2, no. 6, pp. 762-766, 2012.

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