Xiaojun Xu
School of information and communication engineering, Beijing University of Posts and Telecommunications, Beijing, China
Yingli Lv
Hebei Institute of Architecture and Civil Engineering, Zhang Jiakou, P.R. China
ABSTRACT
With the rapid development of multimedia technologies, man-made object detection is one of the important applications. An improved LDA approach was used to learn and recognize man-made and natural scene categories. It represent the image of a scene by a collection of local regions, denoted as codewords, each region is represented as part of a theme. It learns the theme distributions as well as the codewords distribution over the themes. At last Support Vector Machine (SVM) classifier was used to image database for the man-made object detection. We report satisfactory categorization performances on a large set of image database.
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How to cite this article
Xiaojun Xu and Yingli Lv, 2013. Man-made Object Detection Based on Latent Dirichlet Allocation. Information Technology Journal, 12: 6258-6262.
DOI: 10.3923/itj.2013.6258.6262
URL: https://scialert.net/abstract/?doi=itj.2013.6258.6262
DOI: 10.3923/itj.2013.6258.6262
URL: https://scialert.net/abstract/?doi=itj.2013.6258.6262
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