TY - GEN
T1 - An expandable hierarchical statistical framework for count data modeling and its application to object classification
AU - Bakhtiari, Ali Shojaee
AU - Bouguila, Nizar
N1 - Copyright:
Copyright 2012 Elsevier B.V., All rights reserved.
PY - 2011
Y1 - 2011
N2 - The problem that we address in this paper is that of learning hierarchical object categories. Indeed, Digital media technology generates huge amount of non-textual information. Categorizing this information is a challenging task which has served important applications. An important part of this nontextual information is composed of images and videos which consists of various objects each of which may be used to effectively classify the images or videos. Object classification in computer vision can be looked upon from several different perspectives. From the structural perspective object classification models can be divided into flat and hierarchical models. Many of the well-known hierarchical structures proposed so far are based on the Dirichlet distribution. In this work, however, we present a generative hierarchical statistical model based on generalized Dirichlet distribution for the categorization of visual objects modeled as a set of local features describing patches detected using interest points detector. We demonstrate the effectiveness of the proposed model through extensive experiments.
AB - The problem that we address in this paper is that of learning hierarchical object categories. Indeed, Digital media technology generates huge amount of non-textual information. Categorizing this information is a challenging task which has served important applications. An important part of this nontextual information is composed of images and videos which consists of various objects each of which may be used to effectively classify the images or videos. Object classification in computer vision can be looked upon from several different perspectives. From the structural perspective object classification models can be divided into flat and hierarchical models. Many of the well-known hierarchical structures proposed so far are based on the Dirichlet distribution. In this work, however, we present a generative hierarchical statistical model based on generalized Dirichlet distribution for the categorization of visual objects modeled as a set of local features describing patches detected using interest points detector. We demonstrate the effectiveness of the proposed model through extensive experiments.
UR - http://www.scopus.com/inward/record.url?scp=84855818661&partnerID=8YFLogxK
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U2 - 10.1109/ICTAI.2011.128
DO - 10.1109/ICTAI.2011.128
M3 - Conference contribution
AN - SCOPUS:84855818661
SN - 9780769545967
T3 - Proceedings - International Conference on Tools with Artificial Intelligence, ICTAI
SP - 817
EP - 824
BT - Proceedings - 2011 23rd IEEE International Conference on Tools with Artificial Intelligence, ICTAI 2011
T2 - 23rd IEEE International Conference on Tools with Artificial Intelligence, ICTAI 2011
Y2 - 7 November 2011 through 9 November 2011
ER -