Improved Object Recognition with Decision Trees Using Subspace Clustering

Simple item page
datacite.alternateIdentifier.citationJOURNAL OF ADVANCED COMPUTATIONAL INTELLIGENCE AND INTELLIGENT INFORMATICS,Vol.20,41-48,2016
datacite.creatorPeralta Márquez, Billy
datacite.creatorCaro Saldivia, Luis
datacite.date2016
datacite.subject.englishdecision trees
datacite.subject.englishobject recognition
datacite.subject.englishsubspace clustering
datacite.subject.englishrandom forest
datacite.titleImproved Object Recognition with Decision Trees Using Subspace Clustering
dc.date.accessioned2021-04-30T16:25:25Z
dc.date.available2021-04-30T16:25:25Z
dc.description.abstractGeneric 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.
dc.identifier.urihttp://repositoriodigital.uct.cl/handle/10925/2527
dc.language.isoen
dc.publisherFUJI TECHNOLOGY PRESS LTD
dc.sourceJOURNAL OF ADVANCED COMPUTATIONAL INTELLIGENCE AND INTELLIGENT INFORMATICS
oaire.resourceTypeArticle
uct.catalogadorWOS
uct.indizacionESCI
Files
Collections
Migración Web of Science