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Please use this identifier to cite or link to this item: http://ntour.ntou.edu.tw:8080/ir/handle/987654321/45162

Title: PLSA-based Sparse Representation for Object Classification
Authors: Yilin Yan
Jun-Wei Hsieh
Hui-Fen Chiang
Shyi-Chyi Cheng
Duan-Yu Chen
Contributors: 國立臺灣海洋大學:資訊工程學系
NTOU:Department of Computer Science and Engineering
Keywords: sparse representation
object classification
Date: 2014-08
Issue Date: 2018-01-31T03:33:20Z
Publisher: Pattern Recognition (ICPR), 2014 22nd International Conference on
Abstract: Abstract:
This paper proposes a novel object classification method which uses the concept of probabilistic latent semantic analysis (pLSA) to overcome the problem of sparse representation in data classification. Sparse representation is widely used and quite successful in many vision-based applications. However, it needs to calculate the sparse reconstruction cost (SRC) of each sample to find the best candidate. Because an optimization process is involved, it is very inefficient. In addition, it uses only the residual and does not consider the arrangement (or distribution) of combination coefficients of visual codes in classification. Thus, it often fails to classify categories if they are similar. In this paper, the pLSA concept is first introduced into the sparse representation to build a new classifier without using the SRC measure. The weakness of the pLSA scheme is the use of EM algorithm for updating the posteriori probability of latent class. Because it is very time-consuming, a novel weighting voting strategy is introduced to improve the pLSA scheme for recognizing objects in real time. The advantages of this classifier are: the accuracy is much higher than the SRC scheme and the efficiency is real-time in data classification. Two applications are demonstrated in this paper to prove the superiority of the new classifier, i.e., vehicle make and model recognition, and action analysis.
URI: http://ntour.ntou.edu.tw:8080/ir/handle/987654321/45162
Appears in Collections:[資訊工程學系] 演講及研討會

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