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JACIII Vol.20 No.1 pp. 41-48
doi: 10.20965/jaciii.2016.p0041
(2016)

Paper:

Improved Object Recognition with Decision Trees Using Subspace Clustering

Billy Peralta and Luis Alberto Caro

Catholic University of Temuco
Rudecindo Ortega 02950, Temuco, Chile

Received:
July 23, 2015
Accepted:
October 23, 2015
Online released:
January 19, 2016
Published:
January 20, 2016
Keywords:
decision trees, object recognition, subspace clustering, random forest
Abstract

Generic object recognition algorithms usually require complex classificationmodels because of intrinsic difficulties arising from problems such as changes in pose, lighting conditions, or partial occlusions. Decision trees present an inexpensive alternative for classification tasks and offer the advantage of being simple to understand. On the other hand, a common scheme for object recognition is given by the appearances of visual words, also known as the bag-of-words method. Although multiple co-occurrences of visual words are more informative regarding visual classes, a comprehensive evaluation of such combinations is unfeasible because it would result in a combinatorial explosion. In this paper, we propose to obtain the multiple co-occurrences of visual words using a variant of the CLIQUE subspace-clustering algorithm for improving the object recognition performance of simple decision trees. Experiments on standard object datasets show that our method improves the accuracy of the classification of generic objects in comparison to traditional decision tree techniques that are similar, in terms of accuracy, to ensemble techniques. In future we plan to evaluate other variants of decision trees, and apply other subspace-clustering algorithms.

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Last updated on Jul. 26, 2017