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).
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.